Exploring Neural Models for Query-Focused Summarization

Anonymous

Paper Link: https://openreview.net/forum?id=jTm9F6FB63z

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released datasets, such as QMSum or AQuaMuSe, facilitate research efforts in QFS, the field lacks a comprehensive study of the broad space of applicable modeling methods. In this paper we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models. Within those categories, we investigate existing models and explore strategies for transfer learning. We also present two modeling extensions that achieve state-of-the-art performance on the QMSum dataset, up to a margin of 3.38 ROUGE-1, 3.72 ROUGE2, and 3.28 ROUGE-L when combined with transfer learning strategies. Results from human evaluation suggest that the best models produce more comprehensive and factually consistent summaries compared to a baseline model. Code and checkpoints are made publicly available: https://github.com/salesforce/query-focused-sum.

Copyright Consent Signature (type Name Or NA If Not Transferrable): Jesse Vig

Copyright Consent Name And Address: Salesforce, Salesforce Tower, 415 Mission Street, 3rd Floor, San Francisco, CA 94105