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Error Analysis of using BART for Multi-Document Summarization: A Study for English and German Language

Timo Johner

Abhik Jana

Chris Biemann

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Ingår i: Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), May 31-June 2, 2021.

Linköping Electronic Conference Proceedings 178:43, s. 391-397

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Publicerad: 2021-05-21

ISBN: 978-91-7929-614-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Recent research using pre-trained language models for multi-document summarization task lacks deep investigation of potential erroneous cases and their possible application on other languages. In this work, we apply a pre-trained language model (BART) for multi-document summarization (MDS) task using both fine-tuning and without fine-tuning. We use two English datasets and one German dataset for this study. First, we reproduce the multi-document summaries for English language by following one of the recent studies. Next, we show the applicability of the model to German language by achieving state-of-the-art performance on German MDS. We perform an in-depth error analysis of the followed approach for both languages, which leads us to identifying most notable errors, from made-up facts and topic delimitation, and quantifying the amount of extractiveness.

Nyckelord

low-resource language, multi-document summarization, BART, error analysis

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