Search Results for author: Benjamin Sznajder

Found 15 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

Using Question Answering Rewards to Improve Abstractive 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.

Abstractive Text Summarization Question Answering

Label-Efficient Model Selection for Text Generation

no code implementations12 Feb 2024 Shir Ashury-Tahan, Benjamin Sznajder, Leshem Choshen, Liat Ein-Dor, Eyal Shnarch, Ariel Gera

DiffUse reduces the required amount of preference annotations, thus saving valuable time and resources in performing evaluation.

Model Selection Text Generation

The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers

1 code implementation2 May 2023 Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch

Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.

Language Modelling Text Generation

Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization

no code implementations29 Mar 2022 Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim

We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.

Decision Making

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

Financial Event Extraction Using Wikipedia-Based Weak Supervision

no code implementations WS 2019 Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.

BIG-bench Machine Learning Event Extraction

Argument Invention from First Principles

no code implementations ACL 2019 Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim

In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.

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.

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.

Unsupervised corpus--wide claim detection

no code implementations WS 2017 Ran Levy, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, Noam Slonim

Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration.

Argument Mining Decision Making +1

Cannot find the paper you are looking for? You can Submit a new open access paper.