Search Results for author: Ori Shapira

Found 17 papers, 10 papers with code

Measuring Linguistic Synchrony in Psychotherapy

no code implementations NAACL (CLPsych) 2022 Natalie Shapira, Dana Atzil-Slonim, Rivka Tuval Mashiach, Ori Shapira

We study the phenomenon of linguistic synchrony between clients and therapists in a psychotherapy process.


SummHelper: Collaborative Human-Computer Summarization

no code implementations16 Aug 2023 Aviv Slobodkin, Niv Nachum, Shmuel Amar, Ori Shapira, Ido Dagan

Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process.

Text Summarization

Proposition-Level Clustering for Multi-Document Summarization

2 code implementations NAACL 2022 Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan

Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.

Clustering Document Summarization +2

Multi-Document Keyphrase Extraction: Dataset, Baselines and Review

1 code implementation3 Oct 2021 Ori Shapira, Ramakanth Pasunuru, Ido Dagan, Yael Amsterdamer

Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications.

Keyphrase Extraction

Extending Multi-Document Summarization Evaluation to the Interactive Setting

1 code implementation NAACL 2021 Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer, Ido Dagan

In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session.

Document Summarization Multi-Document Summarization

Evaluating Interactive Summarization: an Expansion-Based Framework

no code implementations17 Sep 2020 Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer, Ido Dagan

Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results.

Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline

1 code implementation CoNLL (EMNLP) 2021 Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan

Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.

Clustering 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

Better Rewards Yield Better Summaries: Learning to Summarise Without References

2 code implementations IJCNLP 2019 Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, Iryna Gurevych

Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.

Reinforcement Learning (RL)

How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature

no code implementations WS 2019 Simeng Sun, Ori Shapira, Ido Dagan, Ani Nenkova

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths.

Interactive Abstractive Summarization for Event News Tweets

no code implementations EMNLP 2017 Ori Shapira, Hadar Ronen, Meni Adler, Yael Amsterdamer, Judit Bar-Ilan, Ido Dagan

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts.

Abstractive Text Summarization Document Summarization +1

A Consolidated Open Knowledge Representation for Multiple Texts

1 code implementation WS 2017 Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martinez Camara, Iryna Gurevych, Ido Dagan

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.

Lexical Entailment Open Information Extraction

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