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Under special circumstances, summaries should conform to a particular stylewith patterns, such as court judgments and abstracts in academic papers. Tothis end, the prototype document-summary pairs can be utilized to generatebetter summaries. There are two main challenges in this task: (1) the modelneeds to incorporate learned patterns from the prototype, but (2) should avoidcopying contents other than the patternized words---such as irrelevantfacts---into the generated summaries. To tackle these challenges, we design amodel named Prototype Editing based Summary Generator (PESG). PESG first learnssummary patterns and prototype facts by analyzing the correlation between aprototype document and its summary. Prototype facts are then utilized to helpextract facts from the input document. Next, an editing generator generates newsummary based on the summary pattern or extracted facts. Finally, to addressthe second challenge, a fact checker is used to estimate mutual informationbetween the input document and generated summary, providing an additionalsignal for the generator. Extensive experiments conducted on a large-scalereal-world text summarization dataset show that PESG achieves thestate-of-the-art performance in terms of both automatic metrics and humanevaluations.

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