Search Results for author: Ran Levy

Found 13 papers, 1 papers with code

The Power of Summary-Source Alignments

1 code implementation2 Jun 2024 Ori Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar, Mohit Bansal, Jacob Goldberger, Ran Levy, Ido Dagan

Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.

Document Summarization Multi-Document Summarization +1

Multi-Review Fusion-in-Context

no code implementations22 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

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 +1

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 +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

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

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