How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

TL;DR

This paper introduces PESG, a novel model that leverages prototype document-summary pairs and editing techniques to generate style-conforming summaries while avoiding irrelevant content, achieving state-of-the-art results.

Contribution

The paper proposes a prototype editing framework for abstractive summarization that effectively incorporates learned patterns and prototype facts, improving summary quality.

Findings

PESG outperforms existing models on large-scale datasets.

The fact checker enhances relevance and accuracy of summaries.

Prototype-based editing improves style conformity.

Abstract

Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.