AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization

• URL: http://arxiv.org/abs/2010.12694v1
• Date: Fri, 23 Oct 2020 22:38:18 GMT
• Title: AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
• Authors: Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, Eugene Ie
• Abstract summary: We propose a scalable approach called AQuaMuSe to automatically mine qMDS examples from question answering datasets and large document corpora. We publicly release a specific instance of an AQuaMuSe dataset with 5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.
• Score: 17.098075160558576
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Summarization is the task of compressing source document(s) into coherent and succinct passages. This is a valuable tool to present users with concise and accurate sketch of the top ranked documents related to their queries. Query-based multi-document summarization (qMDS) addresses this pervasive need, but the research is severely limited due to lack of training and evaluation datasets as existing single-document and multi-document summarization datasets are inadequate in form and scale. We propose a scalable approach called AQuaMuSe to automatically mine qMDS examples from question answering datasets and large document corpora. Our approach is unique in the sense that it can general a dual dataset -- for extractive and abstractive summaries both. We publicly release a specific instance of an AQuaMuSe dataset with 5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl. Extensive evaluation of the dataset along with baseline summarization model experiments are provided.

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