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XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages

arXiv 2303.12308 ↗First posted 22 Mar 2023cs.CLcited by 1

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Abstract (arXiv)

Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization ineffective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference articles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ~69K Wikipedia articles covering five domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specific LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summarization to coarsely identify salient information followed by a neural abstractive model to generate the section-specific text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 22 Mar 2023.

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31 of 44 references.

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19 more
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  1. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18