Rahul Jha


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AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Keping Bi | Rahul Jha | Bruce Croft | Asli Celikyilmaz
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step. Previous work shows the efficacy of jointly scoring and selecting sentences with neural sequence generation models. It is, however, not well-understood if the gain is due to better encoding techniques or better redundancy reduction approaches. Similarly, the contribution of salience versus diversity components on the created summary is not studied well. Building on the state-of-the-art encoding methods for summarization, we present two adaptive learning models: AREDSUM-SEQ that jointly considers salience and novelty during sentence selection; and a two-step AREDSUM-CTX that scores salience first, then learns to balance salience and redundancy, enabling the measurement of the impact of each aspect. Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling diversity explicitly in a separate step, AREDSUM-CTX achieves significantly better performance than AREDSUM-SEQ as well as state-of-the-art extractive summarization baselines.

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QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Ming Zhong | Da Yin | Tao Yu | Ahmad Zaidi | Mutethia Mutuma | Rahul Jha | Ahmed Hassan Awadallah | Asli Celikyilmaz | Yang Liu | Xipeng Qiu | Dragomir Radev
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/QMSum.

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GO FIGURE: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel | Asli Celikyilmaz | Rahul Jha | Yejin Choi | Jianfeng Gao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
Rahul Jha | Keping Bi | Yang Li | Mahdi Pakdaman | Asli Celikyilmaz | Ivan Zhiboedov | Kieran McDonald
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems

We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains. Current summarization evaluation datasets are single-domain and focused on a few domains for which naturally occurring summaries can be easily found, such as news and scientific articles. These are not sufficient for training and evaluation of summarization models for use in document management and information retrieval systems, which need to deal with documents from multiple domains. Compared to other annotation methods such as Relative Utility and Pyramid, Artemis is more tractable because judges don’t need to look at all the sentences in a document when making an importance judgment for one of the sentences, while providing similarly rich sentence importance annotations. We describe the annotation process in detail and compare it with other similar evaluation systems. We also present analysis and experimental results over a sample set of 532 annotated documents.


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Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios
Kunho Kim | Rahul Jha | Kyle Williams | Alex Marin | Imed Zitouni
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.


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Bag of Experts Architectures for Model Reuse in Conversational Language Understanding
Rahul Jha | Alex Marin | Suvamsh Shivaprasad | Imed Zitouni
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants. Since each new domain requires a different set of slots, the annotation costs for labeling data for training slot tagging models increases rapidly as the number of domains grow. To tackle this, we describe Bag of Experts (BoE) architectures for model reuse for both LSTM and CRF based models. Extensive experimentation over a dataset of 10 domains drawn from data relevant to our commercial personal digital assistant shows that our BoE models outperform the baseline models with a statistically significant average margin of 5.06% in absolute F1-score when training with 2000 instances per domain, and achieve an even higher improvement of 12.16% when only 25% of the training data is used.


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Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
Dragomir Radev | Amanda Stent | Joel Tetreault | Aasish Pappu | Aikaterini Iliakopoulou | Agustin Chanfreau | Paloma de Juan | Jordi Vallmitjana | Alejandro Jaimes | Rahul Jha | Robert Mankoff
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.


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Content Models for Survey Generation: A Factoid-Based Evaluation
Rahul Jha | Catherine Finegan-Dollak | Ben King | Reed Coke | Dragomir Radev
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Heterogeneous Networks and Their Applications: Scientometrics, Name Disambiguation, and Topic Modeling
Ben King | Rahul Jha | Dragomir R. Radev
Transactions of the Association for Computational Linguistics, Volume 2

We present heterogeneous networks as a way to unify lexical networks with relational data. We build a unified ACL Anthology network, tying together the citation, author collaboration, and term-cooccurence networks with affiliation and venue relations. This representation proves to be convenient and allows problems such as name disambiguation, topic modeling, and the measurement of scientific impact to be easily solved using only this network and off-the-shelf graph algorithms.


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Experimental Results on the Native Language Identification Shared Task
Amjad Abu-Jbara | Rahul Jha | Eric Morley | Dragomir Radev
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Random Walk Factoid Annotation for Collective Discourse
Ben King | Rahul Jha | Dragomir Radev | Robert Mankoff
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A System for Summarizing Scientific Topics Starting from Keywords
Rahul Jha | Amjad Abu-Jbara | Dragomir Radev
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Identifying the Semantic Orientation of Foreign Words
Ahmed Hassan | Amjad Abu-Jbara | Rahul Jha | Dragomir Radev
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies