Ori Shapira


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iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
Eran Hirsch | Alon Eirew | Ori Shapira | Avi Caciularu | Arie Cattan | Ori Ernst | Ramakanth Pasunuru | Hadar Ronen | Mohit Bansal | Ido Dagan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce iFᴀᴄᴇᴛSᴜᴍ, a web application for exploring topical document collections. iFᴀᴄᴇᴛSᴜᴍ integrates interactive summarization together with faceted search, by providing a novel faceted navigation scheme that yields abstractive summaries for the user’s selections. This approach offers both a comprehensive overview as well as particular details regard-ing subtopics of choice. The facets are automatically produced based on cross-document coreference pipelines, rendering generic concepts, entities and statements surfacing in the source texts. We analyze the effectiveness of our application through small-scale user studies that suggest the usefulness of our tool.

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Hebrew Psychological Lexicons
Natalie Shapira | Dana Atzil-Slonim | Daniel Juravski | Moran Baruch | Dana Stolowicz-Melman | Adar Paz | Tal Alfi-Yogev | Roy Azoulay | Adi Singer | Maayan Revivo | Chen Dahbash | Limor Dayan | Tamar Naim | Lidar Gez | Boaz Yanai | Adva Maman | Adam Nadaf | Elinor Sarfati | Amna Baloum | Tal Naor | Ephraim Mosenkis | Badreya Sarsour | Jany Gelfand Morgenshteyn | Yarden Elias | Liat Braun | Moria Rubin | Matan Kenigsbuch | Noa Bergwerk | Noam Yosef | Sivan Peled | Coral Avigdor | Rahav Obercyger | Rachel Mann | Tomer Alper | Inbal Beka | Ori Shapira | Yoav Goldberg
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

We introduce a large set of Hebrew lexicons pertaining to psychological aspects. These lexicons are useful for various psychology applications such as detecting emotional state, well being, relationship quality in conversation, identifying topics (e.g., family, work) and many more. We discuss the challenges in creating and validating lexicons in a new language, and highlight our methodological considerations in the data-driven lexicon construction process. Most of the lexicons are publicly available, which will facilitate further research on Hebrew clinical psychology text analysis. The lexicons were developed through data driven means, and verified by domain experts, clinical psychologists and psychology students, in a process of reconciliation with three judges. Development and verification relied on a dataset of a total of 872 psychotherapy session transcripts. We describe the construction process of each collection, the final resource and initial results of research studies employing this resource.

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Extending Multi-Document Summarization Evaluation to the Interactive Setting
Ori Shapira | Ramakanth Pasunuru | Hadar Ronen | Mohit Bansal | Yael Amsterdamer | Ido Dagan
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. Our framework includes a procedure of collecting real user sessions, as well as evaluation measures relying on summarization standards, but adapted to reflect interaction. All of our solutions and resources are available publicly as a benchmark, allowing comparison of future developments in interactive summarization, and spurring progress in its methodological evaluation. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will serve as part of our benchmark. Our extensive experimentation and analysis motivate the proposed evaluation framework design and support its viability.

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Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Ori Ernst | Ori Shapira | Ramakanth Pasunuru | Michael Lepioshkin | Jacob Goldberger | Mohit Bansal | Ido Dagan
Proceedings of the 25th Conference on Computational Natural Language Learning

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.


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Better Rewards Yield Better Summaries: Learning to Summarise Without References
Florian Böhm | Yang Gao | Christian M. Meyer | Ori Shapira | Ido Dagan | Iryna Gurevych
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reinforcement Learning (RL)based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summaries with higher human ratings. The learned reward function and our source code are available at https://github.com/yg211/summary-reward-no-reference.

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How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
Simeng Sun | Ori Shapira | Ido Dagan | Ani Nenkova
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.

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Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Ori Shapira | David Gabay | Yang Gao | Hadar Ronen | Ramakanth Pasunuru | Mohit Bansal | Yael Amsterdamer | Ido Dagan
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the required expertise, researchers resorted to cheaper and less thorough manual evaluation methods, such as Responsiveness and pairwise comparison, attainable via crowdsourcing. We revisit the Pyramid approach, proposing a lightweight sampling-based version that is crowdsourcable. We analyze the performance of our method in comparison to original expert-based Pyramid evaluations, showing higher correlation relative to the common Responsiveness method. We release our crowdsourced Summary-Content-Units, along with all crowdsourcing scripts, for future evaluations.


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Evaluating Multiple System Summary Lengths: A Case Study
Ori Shapira | David Gabay | Hadar Ronen | Judit Bar-Ilan | Yael Amsterdamer | Ani Nenkova | Ido Dagan
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.


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Interactive Abstractive Summarization for Event News Tweets
Ori Shapira | Hadar Ronen | Meni Adler | Yael Amsterdamer | Judit Bar-Ilan | Ido Dagan
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.

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A Consolidated Open Knowledge Representation for Multiple Texts
Rachel Wities | Vered Shwartz | Gabriel Stanovsky | Meni Adler | Ori Shapira | Shyam Upadhyay | Dan Roth | Eugenio Martinez Camara | Iryna Gurevych | Ido Dagan
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.