A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics

no code implementations • WS 2014 • Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy, Ruty Rinott, Dan Gutfreund, Noam Slonim

Claims on demand -- an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora

no code implementations • COLING 2014 • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund

Decision Making

Context Dependent Claim Detection

no code implementations • COLING 2014 • Ran Levy, Yonatan Bilu, Daniel Hershcovich, Ehud Aharoni, Noam Slonim

Decision Making

TR9856: A Multi-word Term Relatedness Benchmark

no code implementations • IJCNLP 2015 • Ran Levy, Liat Ein-Dor, Shay Hummel, Ruty Rinott, Noam Slonim

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

GRASP: Rich Patterns for Argumentation Mining

no code implementations • EMNLP 2017 • Eyal Shnarch, Ran Levy, Vikas Raykar, Noam Slonim

A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term].

Document Classification

Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution

no code implementations • LREC 2018 • Liat Ein Dor, Alon Halfon, Yoav Kantor, Ran Levy, Yosi Mass, Ruty Rinott, Eyal Shnarch, Noam Slonim

Entity Linking Learning-To-Rank

Towards an argumentative content search engine using weak supervision

no code implementations • COLING 2018 • Ran Levy, Ben Bogin, Shai Gretz, Ranit Aharonov, Noam Slonim

Our results clearly indicate that the system is able to successfully generalize from the weak signal, outperforming previously reported results in terms of both precision and coverage.

Argument Mining Decision Making +1

Massive Multi-Document Summarization of Product Reviews with Weak Supervision

no code implementations • 22 Jul 2020 • Ori Shapira, Ran Levy

Product reviews summarization is a type of Multi-Document Summarization (MDS) task in which the summarized document sets are often far larger than in traditional MDS (up to tens of thousands of reviews).

Document Summarization Multi-Document Summarization

Identifying Helpful Sentences in Product Reviews

no code implementations • NAACL 2021 • Iftah Gamzu, Hila Gonen, Gilad Kutiel, Ran Levy, Eugene Agichtein

This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness.

Document Summarization Multi-Document Summarization +1

PASS: Perturb-and-Select Summarizer for Product Reviews

no code implementations • ACL 2021 • Nadav Oved, Ran Levy

We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme.

Multi-Review Fusion-in-Context

no code implementations • 22 Mar 2024 • Aviv Slobodkin, Ori Shapira, Ran Levy, Ido Dagan

This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content.

Long Form Question Answering Text Generation