From Neural Sentence Summarization to Headline Generation:
A Coarse-to-Fine Approach
Abstract
Headline generation is a task of abstractive text
summarization, and previously suffers from the
immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating
informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we
investigate the extension of sentence summarization models to the document headline generation
task. The challenge is that extending the sentence
summarization model to consider more document
information will mostly confuse the model and
hurt the performance. In this paper, we propose a
coarse-to-fine approach, which first identifies the
important sentences of a document using document summarization techniques, and then exploits
a multi-sentence summarization model with hierarchical attention to leverage the important sentences
for headline generation. Experimental results on
a large real dataset demonstrate the proposed approach significantly improves the performance
of neural sentence summarization models on the
headline generation task