LMGQS: A Large-scale Dataset for Query-focused Summarization

• URL: http://arxiv.org/abs/2305.13086v1
• Date: Mon, 22 May 2023 14:53:45 GMT
• Title: LMGQS: A Large-scale Dataset for Query-focused Summarization
• Authors: Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Chenguang Zhu, Michael Zeng
• Abstract summary: We convert four generic summarization benchmarks into a new QFS benchmark dataset, LMGQS. We establish baselines with state-of-the-art summarization models. We achieve state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks.
• Score: 77.6179359525065
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets in the form of documents, queries, and summaries has hindered model development in this area. In contrast, multiple large-scale high-quality datasets for generic summarization exist. We hypothesize that there is a hidden query for each summary sentence in a generic summarization annotation, and we utilize a large-scale pretrained language model to recover it. In this way, we convert four generic summarization benchmarks into a new QFS benchmark dataset, LMGQS, which consists of over 1 million document-query-summary samples. We thoroughly investigate the properties of our proposed dataset and establish baselines with state-of-the-art summarization models. By fine-tuning a language model on LMGQS, we achieve state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks, demonstrating the high quality and diversity of LMGQS.

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