Guy Feigenblat


2020

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Proceedings of the First Workshop on Scholarly Document Processing
Muthu Kumar Chandrasekaran | Anita de Waard | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Petr Knoth | David Konopnicki | Philipp Mayr | Robert M. Patton | Michal Shmueli-Scheuer
Proceedings of the First Workshop on Scholarly Document Processing

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Overview of the First Workshop on Scholarly Document Processing (SDP)
Muthu Kumar Chandrasekaran | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Philipp Mayr | Michal Shmueli-Scheuer | Anita de Waard
Proceedings of the First Workshop on Scholarly Document Processing

Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website: https://ornlcda.github.io/SDProc

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Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm
Muthu Kumar Chandrasekaran | Guy Feigenblat | Eduard Hovy | Abhilasha Ravichander | Michal Shmueli-Scheuer | Anita de Waard
Proceedings of the First Workshop on Scholarly Document Processing

We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.

2019

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A Summarization System for Scientific Documents
Shai Erera | Michal Shmueli-Scheuer | Guy Feigenblat | Ora Peled Nakash | Odellia Boni | Haggai Roitman | Doron Cohen | Bar Weiner | Yosi Mass | Or Rivlin | Guy Lev | Achiya Jerbi | Jonathan Herzig | Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Francesca Bonin | David Konopnicki
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.

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An Editorial Network for Enhanced Document Summarization
Edward Moroshko | Guy Feigenblat | Haggai Roitman | David Konopnicki
Proceedings of the 2nd Workshop on New Frontiers in Summarization

We suggest a new idea of Editorial Network – a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. We further suggest an effective way for training the “editor” based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baselines.

2016

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Classifying Emotions in Customer Support Dialogues in Social Media
Jonathan Herzig | Guy Feigenblat | Michal Shmueli-Scheuer | David Konopnicki | Anat Rafaeli | Daniel Altman | David Spivak
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue