  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.
