no code implementations • NAACL (sdp) 2021 • Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Wang
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
no code implementations • EMNLP (sdp) 2020 • Muthu Kumar Chandrasekaran, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard
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.
no code implementations • EMNLP (sdp) 2020 • Muthu Kumar Chandrasekaran, Guy Feigenblat, Eduard Hovy, Abhilasha Ravichander, Michal Shmueli-Scheuer, Anita de Waard
We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm.
no code implementations • Findings (EMNLP) 2021 • Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization Unsupervised Extractive Summarization
1 code implementation • Findings (EMNLP) 2021 • Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi
Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.
no code implementations • sdp (COLING) 2022 • Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard, Lucy Lu Wang
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
1 code implementation • sdp (COLING) 2022 • Arman Cohan, Guy Feigenblat, Tirthankar Ghosal, Michal Shmueli-Scheuer
We present the main findings of MuP 2022 shared task, the first shared task on multi-perspective scientific document summarization.
1 code implementation • 23 Nov 2021 • Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization Unsupervised Extractive Summarization
1 code implementation • 7 Oct 2021 • Odellia Boni, Guy Feigenblat, Guy Lev, Michal Shmueli-Scheuer, Benjamin Sznajder, David Konopnicki
We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources.
Ranked #1 on Document Summarization on HowSumm-Step
no code implementations • Findings (ACL) 2021 • Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Sachindra Joshi, David Konopnicki
Many conversation datasets have been constructed in the recent years using crowdsourcing.
no code implementations • 10 Feb 2020 • Odellia Boni, Guy Feigenblat, Doron Cohen, Haggai Roitman, David Konopnicki
Researchers and students face an explosion of newly published papers which may be relevant to their work.
no code implementations • IJCNLP 2019 • 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, David Konopnicki
We present a novel system providing summaries for Computer Science publications.
no code implementations • WS 2019 • Edward Moroshko, Guy Feigenblat, Haggai Roitman, David Konopnicki
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.
Ranked #34 on Abstractive Text Summarization on CNN / Daily Mail
no code implementations • 1 Nov 2018 • Haggai Roitman, Guy Feigenblat, David Konopnicki, Doron Cohen, Odellia Boni
We propose Dual-CES -- a novel unsupervised, query-focused, multi-document extractive summarizer.
Extractive Summarization Query-Based Extractive Summarization