How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
This addresses the need for pattern-conforming summaries in specialized domains like legal or academic writing, though it is incremental as it builds on existing prototype-based methods.
The paper tackled the problem of generating summaries that follow specific patterns, such as in court judgments or academic abstracts, by using prototype document-summary pairs, and achieved state-of-the-art performance on a large-scale dataset with improvements in automatic metrics and human evaluations.
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.