AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
arXiv:2010.12694
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.
References in corpus (9)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Teaching Machines to Read and Comprehend
- Universal Sentence Encoder
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
- SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
- WikiHow: A Large Scale Text Summarization Dataset
- A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
- Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
- Query-Based Abstractive Summarization Using Neural Networks