MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440586" target="_blank" >RIV/00216208:11320/21:10440586 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2021.findings-emnlp.133/" target="_blank" >https://aclanthology.org/2021.findings-emnlp.133/</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

Original language description

One of the most challenging aspects of current single-document news summarization is that the summary often contains &apos;extrinsic hallucinations&apos;, i.e., facts that are not present in the source document, which are often derived via world knowledge. This causes summarisation systems to act more like open-ended language models tending to hallucinate facts that are erroneous. In this paper, we mitigate this problem with the help of multiple supplementary resource documents assisting the task. We present a new dataset MiRANews and benchmark existing summarisation models. In contrast to multi-document summarization, which addresses multiple events from several source documents, we still aim at generating a summary for a single document. We show via data analysis that it&apos;s not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles. An error analysis of generated summaries from pretrained

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Findings of the Association for Computational Linguistics: EMNLP 2021

ISBN

978-1-955917-10-0

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

1541-1552

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Punta Cana, Dominican Republic

Event date

Nov 7, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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