Search Results for author: David Konopnicki

Found 23 papers, 4 papers with code

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

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

TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

1 code implementation23 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

HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles

1 code implementation7 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.

Abstractive Text Summarization Document Summarization +1

orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions

no code implementations3 Sep 2020 Guy Lev, Michal Shmueli-Scheuer, Achiya Jerbi, David Konopnicki

Thus, we release orgFAQ, a new dataset composed of $6988$ user questions and $1579$ corresponding FAQs that were extracted from organizations' FAQ webpages in the Jobs domain.

A Study of Human Summaries of Scientific Articles

no code implementations10 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.

Controversy in Context

no code implementations20 Aug 2019 Benjamin Sznajder, Ariel Gera, Yonatan Bilu, Dafna Sheinwald, Ella Rabinovich, Ranit Aharonov, David Konopnicki, Noam Slonim

With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is.

A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints

no code implementations19 Aug 2019 Yosi Mass, Haggai Roitman, Shai Erera, Or Rivlin, Bar Weiner, David Konopnicki

We study the use of BERT for non-factoid question-answering, focusing on the passage re-ranking task under varying passage lengths.

Learning-To-Rank Passage Re-Ranking +2

Bot2Vec: Learning Representations of Chatbots

no code implementations SEMEVAL 2019 Jonathan Herzig, S, Tommy bank, Michal Shmueli-Scheuer, David Konopnicki

Chatbots (i. e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms.

An Editorial Network for Enhanced Document Summarization

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.

Abstractive Text Summarization Document Summarization

Learning Concept Abstractness Using Weak Supervision

no code implementations EMNLP 2018 Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim

We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data.

Detecting Egregious Conversations between Customers and Virtual Agents

no code implementations NAACL 2018 Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski

In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction.

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