Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Feigenblat, Guy"'
Autor:
Feigenblat, Guy, Gunasekara, Chulaka, Sznajder, Benjamin, Joshi, Sachindra, Konopnicki, David, Aharonov, Ranit
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP (2021) 245--260
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary em
Externí odkaz:
http://arxiv.org/abs/2111.11894
Autor:
Boni, Odellia, Feigenblat, Guy, Lev, Guy, Shmueli-Scheuer, Michal, Sznajder, Benjamin, Konopnicki, David
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. This use-case is different from the use-cases c
Externí odkaz:
http://arxiv.org/abs/2110.03179
Autor:
Gunasekara, Chulaka, Feigenblat, Guy, Sznajder, Benjamin, Joshi, Sachindra, Konopnicki, David
Many conversation datasets have been constructed in the recent years using crowdsourcing. However, the data collection process can be time consuming and presents many challenges to ensure data quality. Since language generation has improved immensely
Externí odkaz:
http://arxiv.org/abs/2106.03337
Researchers and students face an explosion of newly published papers which may be relevant to their work. This led to a trend of sharing human summaries of scientific papers. We analyze the summaries shared in one of these platforms Shortscience.org.
Externí odkaz:
http://arxiv.org/abs/2002.03604
Autor:
Erera, Shai, Shmueli-Scheuer, Michal, Feigenblat, Guy, Nakash, Ora Peled, Boni, Odellia, Roitman, Haggai, Cohen, Doron, Weiner, Bar, Mass, Yosi, Rivlin, Or, Lev, Guy, Jerbi, Achiya, Herzig, Jonathan, Hou, Yufang, Jochim, Charles, Gleize, Martin, Bonin, Francesca, Konopnicki, David
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
Externí odkaz:
http://arxiv.org/abs/1908.11152
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. Our network tries to imitate the decision process of a human
Externí odkaz:
http://arxiv.org/abs/1902.10360
We propose Dual-CES -- a novel unsupervised, query-focused, multi-document extractive summarizer. Dual-CES is designed to better handle the tradeoff between saliency and focus in summarization. To this end, Dual-CES employs a two-step dual-cascade op
Externí odkaz:
http://arxiv.org/abs/1811.00436
In a recent paper from SODA11 \cite{kminwise} the authors introduced a general framework for exponential time improvement of \minwise based algorithms by defining and constructing almost \kmin independent family of hash functions. Here we take it a s
Externí odkaz:
http://arxiv.org/abs/1102.3537
Publikováno v:
In Journal of Computer and System Sciences March 2017 84:171-184
Publikováno v:
In Information and Computation 2011 209(4):737-747