AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
This addresses a pervasive need for researchers in natural language processing by providing a crucial resource for qMDS, though it is incremental as it builds on existing datasets and methods for data mining.
The paper tackles the lack of training and evaluation datasets for query-based multi-document summarization (qMDS) by proposing AQuaMuSe, a scalable method to automatically generate such datasets from question answering data and large corpora, resulting in a publicly released dataset with 5,519 summaries linked to an average of 6 documents from 355M Common Crawl documents.
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.