Summary-Oriented Question Generation for Informational Queries

Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles – with further confirmation via a human evaluation.


Introduction
Question answering (QA) systems have experienced dramatic recent empirical improvements due to several factors including novel neural architectures (Chen and Yih, 2020), access to pre-trained contextualized embeddings (Devlin et al., 2019), and the development of large QA training corpora (Rajpurkar et al., 2016;Trischler et al., 2017;Yu et al., 2020). However, despite technological advancements that support more sophisticated questions (Yang et al., 2018;Joshi et al., 2017;Choi et al., 2018;Reddy et al., 2019), many consumers of QA technology in practice tend to ask simple factoid questions when engaging with these systems. Potential explanations for this phenomenon include low expectations set by previous QA systems, limited coverage for more complex questions * Work was done as an intern at Amazon. not changing these expectations, and users simply not possessing sufficient knowledge of the subject of interest to ask more challenging questions. Irrespective of the reason, one potential solution to this dilemma is to provide users with automatically generated suggested questions (SQs) to help users better understand QA system capabilities.
Generating SQs is a specific form of question generation (QG), a long-studied task with many applied use cases -the most frequent purpose being data augmentation for mitigating the high sample complexity of neural QA models (Alberti et al., 2019a). However, the objective of such existing QG systems is to produce large quantities of question/answer pairs for training, which is contrary to that of SQs. The latter seeks to guide users in their research of a particular subject by producing engaging and understandable questions. To this end, we aim to generate questions that are self-explanatory and introductory.
Self-explanatory questions require neither significant background knowledge nor access to documents used for QG to understand the SQ context. For example, existing QG systems may use the text "On December 13, 2013, Beyoncé unexpectedly released her eponymous fifth studio album on the iTunes store without any prior announcement or promotion." to produce the question "Where was the album released?" This kind of question is not uncommon in crowd-sourced datasets (e.g., SQuAD (Rajpurkar et al., 2016)) but do not satisfy the self-explanatory requirement. Clark and Gardner (2018) estimate that 33 % of SQuAD questions are context-dependent. This context-dependency is not surprising, given that annotators observe the underlying documents when generating questions.
Introductory questions are best answered by a larger passage than short spans such that users can learn more about the subject, possibly inspiring follow-up questions (e.g., "Can convalescent plasma help COVID patients?"). However, existing QG methods mostly generate questions while reading the text corpus and tend to produce narrowly focused questions with close syntactic relations to associated answer spans. TriviaQA (Joshi et al., 2017) and HotpotQA (Yang et al., 2018) also provide fine-grained questions, even though reasoning from a larger document context via multi-hop inference. This narrower focus often produces factoid questions peripheral to the main topic of the underlying document and is less useful to a human user seeking information about a target concept.
Conversely, the Natural Question (NQ) dataset (Kwiatkowski et al., 2019) (and similar ones such as MS Marco (Bajaj et al., 2016), GooAQ (Khashabi et al., 2021)) is significantly closer to simulating the desired informationseeking behavior. Questions are generated independently of the corpus by processing search query logs, and the resulting answers can be entities, spans in texts (aka short answers), or entire paragraphs (aka long answers). Thus, the NQ dataset is more suitable as QG training data for generating SQs as long-answer questions that tend to satisfy our self-explanatory and introductory requirements.
To this end, we propose a novel BERT-based Pointer-Generator Network (BERTPGN) trained with the NQ dataset to generate introductory and self-explanatory questions as SQs. Using NQ, we start by creating a QG dataset that contains questions with both short and long answers. We train our BERTPGN model with these two types of context-question pairs together. During inference, the model can generate either short-or long-answer questions as determined by the context. With automatic evaluation metrics such as BLEU (Papineni et al., 2002), we show that for long-answer question generation, our model can produce state-of-the-art performance with 20.1 BLEU-4, 6.2 higher than (Mishra et al., 2020), the current state-of-the-art on this dataset. The short answer question generation performance can reach 28.1 BLEU-4.
We further validate the generalization ability of our BERTPGN model by creating an out-of-domain test set with the CNN/Daily Mail (Hermann et al., 2015). Without human-generated reference questions, automatic evaluation metrics such as BLEU are not usable. We propose to evaluate these questions with a pretrained QA system that produces two novel metrics. The first is answerability, mea-suring the possibility to find answers from given contexts. The second is granularity, indicating whether the answer would be passages or short spans. Finally, we conduct a human evaluation with generated questions of the test set and demonstrate that our BERTPGN model can produce introductory and self-explanatory questions for informationseeking scenarios, even for a new domain that differs from the training data.
The novel contributions of our paper include: • We generate questions, aiming to be both introductory and self-explanatory, to support human information seeking QA sessions. • We propose to use the BERT-based Pointer-Generator Network to generate questions by encoding larger contexts capable of resulting in answer forms including entities, short text spans, and even whole paragraphs. • We evaluate our method, both automatically and with human evaluation, on in-domain Natural Questions and out-of-domain news datasets, providing insights into question generation for information seeking. • We propose a novel evaluation metric with a pretrained QA system for generated SQs when there is no reference question.

Related Work
QG has been studied in multiple application contexts (e.g., generating questions for reading comprehension tests (Heilman and Smith, 2010), generating questions about an image (Mostafazadeh et al., 2016), recommending questions with respect to a news article (Laban et al., 2020)), evaluating summaries (Deutsch et al., 2020;Wang et al., 2020), and using multiple methods (see (Pan et al., 2019) for a recent survey). Early neural models focused on sequence-to-sequence generation based solutions (Serban et al., 2016;Du et al., 2017). The primary directions for improving these early works generally fall into the categories of providing mechanisms to inject answer-aware information into the neural encoder-decoder architectures (Du and Cardie, 2018;Li et al., 2019;Liu et al., 2019;Wang et al., 2020;Sun et al., 2018), encoding larger portions of the answer document as context (Zhao et al., 2018;Tuan et al., 2020), and incorporating richer knowledge sources (Elsahar et al., 2018). These QG methods and the work described in this paper focus on using single-hop QA datasets such as SQuAD (Rajpurkar et al., 2016(Rajpurkar et al., , 2018, NewsQA (Trischler et al., 2017;Hermann et al., 2015), and MS Marco (Bajaj et al., 2016). However, there has also been recent interest in multi-hop QG settings (Yu et al., 2020;Gupta et al., 2020;Malon and Bai, 2020) by using multi-hop QA datasets including HotPotQA (Yang et al., 2018), Trivi-aQA (Joshi et al., 2017), andFreebaseQA (Jiang et al., 2019). Finally, there has been some recent interesting work regarding unsupervised QG, where the goal is to generate QA training data without an existing QG corpus to train better QA models (Lewis et al., 2019;Li et al., 2020).
Most directly related to our work from a motivation perspective is recent research regarding providing SQs in the context of supporting a news chatbot (Laban et al., 2020). However, the focus of this work is not QG, where they essentially use a GPT-2 language model (Radford et al., 2019) trained on SQuAD data for QG and do not evaluate this component independently. Qi et al. (2020) generates questions for information-seeking but not focuses on introductory questions. Most directly related to our work from a conceptual perspective is regarding producing questions for long answer targets (Mishra et al., 2020), which we contrast directly in Section 3. As QG is a generation task, automated evaluation frequently uses metrics such as BLEU (Papineni et al., 2002), METEOR (Lavie and Agarwal, 2007), and ROUGE (Lin, 2004). As these do not explicitly evaluate the requirements of our information-seeking use case, we also evaluate using the output of a trained QA system and conduct human annotator evaluations.

Problem Definition
Given a context X and an answer A, we want to generate a questionQ that satisfies where the context X could be a paragraph or a document that contains answers, rather than sentences as used in (Du and Cardie, 2018;Tuan et al., 2020), while A could be either short spans in X such as entities or noun phrases (referred to as a short answer), or the entire context X (referred to as a long answer).
The long answer QG task targets generating questions that are best answered by the entire context (i.e., paragraph or document) or a summary of the context, which is notably different from  Figure 1: The BERTPGN architecture. The input for the BERT encoder is the context (w/p: word and position embeddinngs) with answer spans (or the whole context in the long answer setting) marked with the answer tagging (t: answer tagging embeddings). The decoder is a combination of BERT as a language model (i.e. has only self-attentions) and a Transformer-based pointergenerator network.
most QG settings where the answer is a short text span and the context is frequently a single sentence. Mishra et al. (2020) also work on the long answer QG setting using the NQ dataset, but their task definition is arg max Q P (Q|X) where they refer to the context X as the long answer. We use their models as baselines.

Methods
We use the BERT-based Pointer-Generator Network (BERTPGN) to generate questions. Tuan et al. (2020) use two-layer cross attentions between contexts and answers to encode contexts such as paragraphs when generating questions and show improved results. However, they show that threelayer cross attentions produce worse results. We will show later in the experiment that this is due to a lack of better initialization and that a higher layer is better for long answer question generation. Zhao et al. (2018) use answer tagging from the context instead of combining context and answer. Our model is motivated by these two works (Figure 1).

Context and Answer Encoding
as the input for BERT. We use type embeddings to discriminate between a context and an answer, following Zhao et al. (2018);Tuan et al. (2020). We use t i = 0 to represent 'context-only' and t i = 1 to represent 'both context and answer' for token x i . We do not apply the [CLS] in the beginning since we do not need the pooled output from BERT. We do not use the [SEP] to combine contexts and answers as inputs for BERT since we mark answers in the context with type embeddings.
The sequence output from BERT which forms our context-answer encoding is given by

Question Decoding
The transformer-based Pointer-Generator Network is derived from (See et al., 2017) with adaptations to support transformers (Vaswani et al., 2017). Denoting LN(·) as layer normalization, MHA(Q, K, V ) as the multi-head attention with three parameters-query, key, and value, FFN(·) as a linear function, and the decoder input at time t: Y (t) = {y j } t j=1 , the decoder self-attention at time t is given by (illustrated with a single-layer transformer simplification) the cross-attention between encoder and decoder is , and the final decoder output is Using the LSTM (Hochreiter and Schmidhuber, 1997) encoder-decoder model, See et al. (2017) compute a generation probability using the encoder context, decoder state, and decoder input. While the transformer decoder cross-attention A (t) C already contains a linear combination between selfattention of decoder input and encoder-decoder cross attention. Thus, we use the combination of the decoder input and cross-attention to compute the generation probability To improve generalization, we also use a separate BERT model as a language model (LM) for the decoder. Even though BERT is not trained to predict the next token (Devlin et al., 2019) as with typical language models (e.g., GPT-2), we still choose BERT as our LM to ensure the COPY mechanism shares the same vocabulary between the encoder and the decoder. 1 We also do not need to process out-of-vocabulary words because we use the BPE (Sennrich et al., 2016;Devlin et al., 2019) tokenization in both the encoder and decoder.

Natural Questions dataset
We use Natural Questions dataset (Kwiatkowski et al., 2019) for training as NQ questions are independent of their supporting documents. NQ has 307,000 training examples, answered and annotated from Wikipedia pages, in a format of a question, a Wikipedia link, long answer candidates, and short answer annotations. 51 % of these questions have no answer for either being invalid or nonevidence in their supporting documents. Another 36 % have long answers that are paragraphs and have corresponding short answers that either spans long answers or being masked as yes-or-no. The remaining 13 % questions only have long answers. We are most interested in the last portion of questions as they are best answered by summaries of their long answers, reflecting the coarse-grained information-seeking behavior. 2 We use paragraphs that contain long answers or short answers in NQ as the context. We do not consider using the whole Wikipedia page, i.e., the document, as the context as most Wikipedia pages are too long to encode: In the NQ training set, there are 8407 tokens at document level on average, while for news articles in the CNN/Daily Mail that we will discuss in Section 5.2, the average document size is 583 (Tuan et al., 2020), which is not much larger than the average size of long answers in NQ (384 tokens).
We also consider the ratio between questions and the context-answer pairs to avoid generating multiple questions based on the same context-answer. After removing questions that have no answers, there are 152,148 questions and 136,450 unique long answers. The average ratio between questions and long answers is around 1.1 questions per paragraph (ratios are in a range of 1 to 47). The average ratio is more reasonable for question generation, comparing to the SQuAD where there are 1.4 questions per sentence on average (Du et al., 2017).

NQ Preprocessing
We extract questions, long answers, and short answer spans from the NQ dataset. We also extract the Wikipedia titles since long answers alone do not  always contain the words from their corresponding titles. We add brackets ('[' and ']') for all possible short answer spans such that we can later extract these spans accordingly to avoid potential position changes due to context preprocessing (e.g., different tokenization). 3 When there is no short answer, we add brackets to the whole long answer. We then concatenate the titles with long answers as contexts. As in (Mishra et al., 2020), we only keep questions with long answers starting from the HTML paragraph tag. After preprocessing (Table 1), we get 110,865 question-context pairs, while Mishra et al. (2020) gets 77,501 pairs since they only keep long answer questions. We split the dataset with a 90/10 ratio for training/validation. We use the original NQ dev set, which contains 7830 questions, as our test set. We follow the same extraction procedure as with the training and validation data modulo two new steps. First, noting that 79 % of Wikipedia pages appearing in the NQ dev set are also present in the NQ training set, we filter all overlapped contexts from the NQ dev set when creating our test set. Second, the original NQ dev set is 5-way annotated; thus, each question may have up to five different long/short answers. We treat each annotation as an independent context, even though they are associated with the same target question. To separately evaluate the QG performance for long answers and short answers, we split test data into long-answer questions (NQ-LA) and short-answer questions (NQ-SA). Finally, we get 4859 test data in total, with 1495 of them only have long answers while the remaining 3364 have both long and short answers while Mishra et al.
(2020) gets 2136 test data from the original dev set. 3 Using brackets here is an arbitrary but functional choice.

News dataset
We use the 12,744 CNN news articles from the CNN/Daily Mail dataset (Hermann et al., 2015)) for the out-of-domain evaluation. We apply the same preprocessing method as in the NQ dataset to create a long-answer test set -News-LA. We use whole news articles, instead of paragraphs, as contexts, considering to generate questions that lead to entire news articles as answers. For each news article, we first remove highlights, which is a human-generated summary, and datelines (e.g., NEW DELHI, India (CNN)). We filter out those news articles that are longer than 490 tokens with the BEP tokenization and those overlapped contextquestion pairs. Finally, we get 3048 data in the News-LA test set.

In-Domain Evaluation with
Generation Metrics

Experiment Setup and Training
We use a BERT-base uncased model (Devlin et al., 2019) that contains 12 hidden layers. The vocabulary contains 30,522 tokens. We create the PGN decoder with another BERT model from the same setting, followed by a 2-layer transformer with 12 heads and 3072 intermediate sizes. The maximum allowed context length is 500, while the maximum question length is 50. We train our model on an Amazon EC2 P3 machine with one Tesla V100 GPU, with the batch size 10, and the learning rate 5 × 10 −5 with the Adam optimizer (Kingma and Ba, 2015) on all parameters of the BERTPGN model (both BERT models are trainable). We train 20 epochs of our model and evaluate with the dev set to select the model according to perplexity. Each epoch takes around 20 minutes to finish. Throughout the paper, we use the implementation of BLEU, METEOR, and ROUGE L by Sharma et al. (2017).

In-Domain Evaluation
We first evaluate our model using BLEU, ME-TEOR, and ROUGE L to compare with Mishra et al. (2020) on long answers (first two rows in Table 2). The transformer-based iwslt de en is a German to English translation model with 6 encoder and decoder layers, 16 encoder and decoder attention heads, 1024 embedding dimension, and 4096 embedding dimension of feed forward network. The other transformer-based multi-source method, which is based on (Libovický et al., 2018), combines each context with a retrieval-based summary   as input. We decode questions from our model using beam search (beam=3). 4 Evaluating on NQ-LA, our BERTPGN model outperforms both existing models substantially with near seven points for all metrics. The performance for short answer questions NQ-SA is even better, with near eight more BLEU-4 points than NQ-LA.

Ablation Study
We first examine the effect of the pointer network from the BERTPGN. We then run ablation study by first removing BERT-LM in the decoder, and independently • removing type IDs from BERT encoder • removing BERT initialization for BERT encoder • substituting BERT encoder with a 2-layer transformer We train our BERTPGN models from scratch for each setting and conduct these ablation studies for NQ-LA and NQ-SA separately (Table 3).
Removing the pointer from the BERTPGN makes the BLEU-4 scores drop for both NQ-LA and NQ-SA more than removing the BERT as the LM in the decoder. Type IDs are more helpful for NQ-SA (approximately a 5-point drop in BLEU-4) than NQ-LA since NQ-SA needs to use type IDs to mark answers. Removing BERT initialization causes notable drops for both NQ-LA (3.6 drops in BLEU-4) and NQ-SA (7.2 in BLEU-4), which implies that BERT achieves better generalization when encoding these considerably long contexts. Another interesting finding is that the NQ-LA is more sensitive to the number of layers of the encoder than NQ-SA. When decreasing the layers to two from twelve, NQ-LA drops by 0.4 in BLEU-4 while NQ-SA drops by 0.2.

Out-of-Domain Evaluation with QA Systems
We use a well-trained question answering system as the evaluation method, given that the automated scoring metrics have two notable drawbacks when evaluating long-answer questions: (1) There are usually multiple valid questions for long-answer question generation as contexts are much longer than previous work. However, most datasets only have one gold question for each context; (2) They cannot measure generated questions when there is no gold question, which is the right problem that we encountered for our News-LA dataset.

The QA Metrics
We use the BERT-joint model (Alberti et al., 2019b) ( Figure 2) for NQ question answering to evaluate our long answer question generation. The BERTjoint model takes the combination a question and the corresponding context as an input, outputs the probability of answer spans and the probability of answer types. For a context of size n, it produces p start and p end for each token, indicating whether this token is a start or end token of an answer span. It then chooses the answer span (i, j) where i < j  that maximizes p start (i) · p end (j) as the probability of the answer. It also defines the probability of no answer to be p start ([CLS]) · p end ([CLS]), i.e., an answer span that starts then stops at the [CLS] token. Furthermore, the BERT-joint model computes the probability of types of the questionundetermined, long answer, short answer, and YESor-NO. This model achieves 66.2 % F1 on NQ long answer test set, which is 10 % better compared to models used in (Kwiatkowski et al., 2019;Parikh et al., 2016). We define the answerability score (s ans ) as log (p ans /p no ans ), and the granularity score (s gra ) as log (p la /p sa ) when evaluating our long answer question generation with the BERTjoint model.

QG Models to Compare
We construct a baseline model to compare as follows. Using the same BERTPGN architecture, we train a model on the SQuAD sentence-question pairs prepared by Du et al. (2017). When generating questions for news articles, we use the first line of each news article as the context, with the assumption that the first line is a genuine summary produced by humans. Notice that the resulting baseline is the state-of-the-art for answer-free (the model does not know the whereabouts of answer spans) question generation with SQuAD (Table 4).
We refer to the model as M SD hereafter. Similarly, we call our BERTPGN model trained on the NQ dataset as M N Q . We use beam search (beam=3) for both models.

Evaluation Results
We show the QA evaluation results in Figure 3. In the context column, M N Q shows a lower answerability score than the baseline model M SD . While granularity scores show a reverse trend, i.e., higher scores for M N Q than those of M SD . This result implies that M N Q generates more coarse-style questions that have long answers, but these questions are considerably more difficult to answer by the QA model, comparing to short-answer questions. It is also reasonable to assume that news articles' summaries are proper answer-candidates for long-answer questions. Highlights in news articles are human-generated summaries, so we also combine the same set of questions with their corresponding highlights as input for the BERT-joint QA system with results shown as the highlights column in Figure 3. The answerability scores drop for both models comparing the column highlights to the column of context, which is reasonable as the models never see highlights when generating questions. However, the baseline method M SD drops more significantly than M N Q , suggesting that the baseline model is more context-dependent while our model M N Q generates more self-explanatory questions. From the granularity scores of highlights, we find that confidence to determine answer types is lower for both models than that of the context column. However, the M N Q still shows higher granularity scores than the M SD .
We map generated questions for the News-LA on a 2D plot with x-axis the answerability score and y-axis the granularity score for both models in Figure 4. They also confirm the negative correlation between answerability and granularity of generated questions. However, the M N Q generates more questions with both positive s ans and s gra than those from M SD , indicating the effectiveness of our model to generate introductory and self-explanatory questions.  We further conduct a human evaluation using MTurk for the News-LA test set to verify that we can generate self-explanatory and introductory questions and that the automatic evaluation in Section 7 agrees with human evaluation. We ask annotators to read news articles and mark true or false for seven statements regarding generated questions. For each context-question pair, these statements include (see examples in Appendix B) • Question is context dependent • Question is irrelevant to the article • Question implies a contradiction to facts present in the article • Question focuses on a peripheral topic • There is a short span to answer the question • The entire article can be an answer • None answer in the article We randomly select 1000 news articles in News-LA to perform our human evaluation with three different annotators per news article. We received three valid annotations for 943 news articles from a set of 224 annotators. We first consider true/false results regarding three metrics -Context, Span, and Entire -considering only when unanimity is reached among annotators (  Table 6: Pearson correlation (1 × 10 −1 ) between human (Section 8) and automatic (Section 7) evaluation.
For each column, we mark the most positive and negative correlated scores in bold text.
of Entire vs. 40 %) while less likely to be answered by spans from news articles (77 % true of Span vs. 89 %) comparing with M SD questions. These human evaluation results confirm that M N Q questions are more self-explanatory and introductory than M SD . We compute the s ans and s gra for the 943 generated questions (Section 7). We then normalize these two scores and conduct a Pearson correlation analysis (Benesty et al., 2009) with human evaluation results. We use all human evaluation results, regardless of agreements among annotators. From Table 6, we find that Span has the strongest positive correlation with the s ans , while None shows the strongest negative correlation -aligning with the findings for answerability. Span also shows the strongest negative correlation with the s gra for both M N Q and M SD , but the highest positive correlation with granularity varies, with Irrelevant for M N Q questions and None for M SD questions.

Conclusion
We tackle the problem of question generation targeted for human information seeking using automatic question answering technology. We focus on generating questions for news articles that can be answered by longer passages rather than short text spans as suggested questions. We build a BERT-based Pointer-Generator Network as the QG model, trained with the Natural Questions dataset. Our method shows state-of-the-art performance in terms of BLEU, METEOR, and ROUGE L scores on our NQ question generation dataset. We then apply our model to the out-of-domain news articles without further training. We use a QA system to evaluate our QG models as there are no gold questions for comparison. We also conduct a human evaluation to confirm the QA evaluation results.

Broader Impact
We describe a method for an autonomous agent to suggest questions based on machine-reading and question generation technology. Operationally, this work focuses on newswire-sourced data where the generated questions are answered by the text -and is applicable to multi-turn search settings. Thus, there are several potentially positive social impacts. By presenting questions with known answers in the text, users can more efficiently learn about topics in the source documents. Our focus on selfexplanatory and introductory questions increases the utility of questions for this purpose.
Conversely, there is potential to bias people toward a subset of the news chosen by a purported fair search engine, which may be more difficult to detect as the provided questions remove some of the article contexts. In principle, this is mitigated by selecting content that maintains high journalistic standards -but such a risk remains if the technology is deployed by bad-faith actors.
The data for our experiments was derived from the widely used Natural Questions (Kwiatkowski et al., 2019) and CNN/Daily Mail (Hermann et al., 2015) datasets, which in turn were derived from public news sourced data. Our evaluation annotations were performed on Amazon Mechanical Turk, where three authors completed a sample task and set a wage corresponding to an expected rate of 15 $/h.

A Appendix
We show several generated questions here. Each frame box contains a news article, with two questions generated by M N Q (showing in bold texts) and M SD respectively. News articles are selected from the CNN/Daily Mail dataset with preprocessing described in Section 5.2. We also compare these generated questions in Table 7.
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• who are the new astronauts on the moon • how many italians walk into a space station in 2013 After several delays, NASA said Friday that space shuttle Discovery is scheduled for launch in five days. The space shuttle Discovery, seen here in January, is now scheduled to launch Wednesday. Commander Lee Archambault and his six crewmates are now scheduled to lift off to the International Space Station at 9:20 p.m. ET Wednesday. NASA said its managers had completed a readiness review for Discovery, which will be making the 28th shuttle mission to the ISS. The launch date had been delayed to allow "additional analysis and particle impact testing associated with a flow-control valve in the shuttle's main engines," the agency said. According to NASA, the readiness review was initiated after damage was found in a valve on the shuttle Endeavour during its November 2008 flight. Three valves have been cleared and installed on Discovery, it said. Discovery is to deliver the fourth and final set of "solar array wings" to the ISS. With the completed array the station will be able to provide enough electricity when the crew size is doubled to six in May, NASA said. The Discovery also will carry a replacement for a failed unit in a system that converts urine to drinkable water, it said. Discovery's 14-day mission will include four spacewalks, NASA said.
• when is the space shuttle discovery coming out • how many days is the space shuttle discovery scheduled to launch Unemployment in Spain has reached 20 percent, meaning 4.6 million people are out of work, the Spanish government announced Friday. The figure,  [a logical consequence , such as the conclusion of a syllogism] Predicted when is the therefore sign used in a syllogism ran away, including Way, Bolton said. The ones who remained told officers they were at the home to film a video. Way was arrested when he returned to the house to get his car, Bolton said. He said the house was dark inside and looked abandoned. "He just ran from the police, and then he decided to come back," according to Bolton. The second man who returned for his vehicle was arrested after police found eight $100 counterfeit bills inside, according to the officer. Way broke into the music scene two years ago with his hit "Crank That (Soulja Boy)." The rapper also describes himself as a producer and entrepreneur.
• what is the meaning of soulja boy tell em • what was deandre cortez way known as The U.S. military is gearing up for a possible influx of Haitians fleeing their earthquake-stricken country at an Army facility not widely known for its humanitarian missions: Guantanamo Bay. Soldiers at the base have set up tents, beds and toilets, awaiting possible orders from the secretary of defense to proceed, according to Maj. Diana Haynie, a spokeswoman for Joint Task Force Guantanamo Bay. "There's no indication of any mass migration from Haiti," Haynie stressed. "We have not been told to conduct migrant operations." But the base is getting ready "as a prudent measure," Haynie said, since "it takes some time to set things up." Guantanamo Bay is about 200 miles from Haiti. Currently, military personnel at the base are helping the earthquake relief effort by shipping bottled water and food from its warehouse. In addition, Gen. Douglas Fraser, commander of U.S. Southern Command, said the Navy has set up a "logistics field," an area to support bigger ships in the region. The military can now use that as a "lily pad" to fly supplies from ships docked at Guantanamo over to Haiti, he said. "Guantanamo Bay proves its value as a strategic hub for the movement of supplies and personnel to the affected areas in Haiti," Haynie said. As part of the precautionary measures to prepare for possible refugees, the Army has BERTPGN-NQ-whole-article BERTPGN-SQuAD-first-line who are the new astronauts on the moon how many italians walk into a space station in 2013 when is the space shuttle discovery coming out how many days is the space shuttle discovery scheduled to launch what is the average unemployment rate in spain what percentage of spain's population is out of work what is the meaning of soulja boy tell em what was deandre cortez way known as where does the us refugees at guantanamo bay come from what is the name of the us military facility in the us what happened to the girl in the texas polygamist ranch what was the name of the texas polygamist ranch who scored the first goal in the premier league which team did everton fc beat to win the premier league's home draw with tottenham on sunday • where does the us refugees at guantanamo bay come from • what is the name of the us military facility in the us A Colorado woman is being pursued as a "person of interest" in connection with phone calls that triggered the raid of a Texas polygamist ranch, authorities said Friday. Rozita Swinton, 33, has been arrested in a case that is not directly related to the Texas raid. Texas Rangers are seeking Rozita Swinton of Colorado Springs, Colorado, "regarding telephone calls placed to a crisis center hot line in San Angelo, Texas, in late March 2008," the Rangers said in a written statement. The raid of the YFZ (Yearning for Zion) Ranch in Eldorado, Texas, came after a caller -who identified herself as a 16-year-old girl -said she had been physically and sexually abused by an adult man with whom she was forced into a "spiritual marriage." The release said a search of Swinton's home in Colorado uncovered evidence that possibly links her to phone calls made about the ranch, run by the Fundamentalist Church of Jesus Christ of Latter-day Saints. "The possibility exists that Rozita Swinton, who has nothing to do with the FLDS church, may have been a woman who made calls and pretended she was the 16-year-old girl named Sarah," CNN's Gary Tuchman reported. Swinton, 33, has been charged in Colorado with false reporting to authorities and is in police custody. Police said that arrest was not directly related to the Texas case. Authorities raided the Texas ranch April 4 and removed 416 children. Officials have been trying to identify the 16-year-old girl, referred to as Sarah, who claimed she had been abused in the phone calls. FLDS members have denied the girl, supposedly named Sarah Jessop Barlow, exists. Some of the FLDS women who spoke with CNN on Monday said they believed the calls were a hoax. While the phone calls initially prompted the raid, officers received a second search warrant based on what they said was evidence of sexual abuse found at the compound. In court documents, investigators described seeing teen girls who appeared pregnant, records that showed men marrying multiple women and accounts of girls being married to adult men when they were as young as 13. A court hearing began Thursday to determine custody of children who were removed from the ranch.
• what happened to the girl in the texas polygamist ranch • what was the name of the texas polygamist ranch Everton scored twice late on and goalkeeper Tim Howard saved an injury-time penalty as they fought back to secure a 2-2 Premier League home draw with Tottenham on Sunday. Jermain Defoe gave the visitors the lead soon after the interval when nipping in front of Tony Hibbert to convert Aaron Lennon's cross at the near post for his 13th goal of the season. And they doubled their advantage soon after when defender Michael Dawson headed home a Niko Kranjcar corner. But Everton got a foothold back in the game when Seamus Coleman's run and cross was converted by fellow-substitute Louis Saha in the 78th minute. And Tim Cahill rescued a point for the home side with four minutes remaining when he stooped low to head home Leighton Baines' bouncing cross. However, there was still further drama to come when Hibbert was penalized for crashing into Wilson Palacios in the area. However, England striker Defoe smashed his penalty too close to Howard and the keeper pulled off a fine save to give out-of-form Everton a morale-boosting point. The result means Tottenham remain in fourth place, behind north London rivals Arsenal, while Everton have now won just one of their last nine league games. In the day's other match, Bobby Zamora scored the only goal of the game as Fulham beat Sunderland 1-0 to move up to eighth place in the table.
• who scored the first goal in the premier league • which team did everton fc beat to win the premier league's home draw with tottenham on sunday

B Human Evaluation Criteria
Question is context dependent Some questions are context-dependent, e.g., • "who intends to boycott the election" -which election?
Jonathan Scharfen, the acting director of CIS, presented the citizenship certificate Tuesday. He hailed Strank as "a true American hero and a wonderful example of the remarkable contribution and sacrifices that immigrants have made to our great republic throughout its history." The question "who presented the american flag raising on iwo jima" focuses on a peripheral topic -the name of the one raising the flag.
While the question "who was awarded a certificate of citizenship raising the u.s. flag" focuses on the main topic -getting a citizenship.
There is a short span to answer the question Given a news: Los Angeles police have launched an internal investigation to determine who leaked a picture that appears to show a bruised and battered Rihanna. Rihanna was allegedly attacked by her boyfriend, singer Chris Brown, before the Grammys on February 8. The close-up photo -showing a woman with contusions on her forehead and below her eyes, and cuts on her lip -was published on the entertainment Web site TMZ Thursday. TMZ said it was a photo of Rihanna. Twenty-one-year-old Rihanna was allegedly attacked by her boyfriend, singer Chris Brown, on a Los Angeles street before the two were to perform at the Grammys on February 8. "The unauthorized release of a domestic violence photograph immediately generated an internal investigation," an L.A. police spokesman said in a statement. "The Los Angeles Police Department takes seriously its duty to maintain the confidentiality of victims of domestic violence. A violation of this type is considered serious misconduct, with penalties up to and including termination." A spokeswoman for Rihanna declined to comment. The chief investigator in the case had told CNN earlier that authorities had tried to guard against leaks. Detective Deshon Andrews said he had kept the case file closely guarded and that no copies had been made of the original photos and documents. Brown was arrested on February 8 in connection with the case and and booked on suspicion of making criminal threats. Authorities are trying to determine whether Brown should face domestic violence-related charges. Brown apologized for the incident this week. "Words cannot begin to express how sorry and saddened I am over what transpired," the 19year-old said in a statement released by his spokesman. "I am seeking the counseling of my pastor, my mother and other loved ones and I am committed, with God's help, to emerging a better person." The question "who have launched an internal investigation of the leaked rihanna's picture" can be answered by "Los Angeles police".