@inproceedings{manevich-etal-2023-multi,
title = "Multi Document Summarization Evaluation in the Presence of Damaging Content",
author = "Manevich, Avshalom and
Carmel, David and
Cohen, Nachshon and
Kravi, Elad and
Shapira, Ori",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.1",
doi = "10.18653/v1/2023.findings-emnlp.1",
pages = "1--12",
abstract = "In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.",
}
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<abstract>In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.</abstract>
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%0 Conference Proceedings
%T Multi Document Summarization Evaluation in the Presence of Damaging Content
%A Manevich, Avshalom
%A Carmel, David
%A Cohen, Nachshon
%A Kravi, Elad
%A Shapira, Ori
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F manevich-etal-2023-multi
%X In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.
%R 10.18653/v1/2023.findings-emnlp.1
%U https://aclanthology.org/2023.findings-emnlp.1
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1
%P 1-12
Markdown (Informal)
[Multi Document Summarization Evaluation in the Presence of Damaging Content](https://aclanthology.org/2023.findings-emnlp.1) (Manevich et al., Findings 2023)
ACL