Bibliographic Details
| Title: |
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization |
| Authors: |
Kulkarni, Sayali, Chammas, Sheide, Zhu, Wan, Sha, Fei, Ie, Eugene |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2010.12694 |
| Accession Number: |
edsarx.2010.12694 |
| Database: |
arXiv |
