Search Results for author: Ran Levy

Found 11 papers, 0 papers with code

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.

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

Massive Multi-Document Summarization of Product Reviews with Weak Supervision

no code implementations22 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

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

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

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

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