Abstract
Automatic headline generation, or automatic one-line summarization, is a sub-task of document summarization with many reported applications in several domains. In this chapter we present a sequence-prediction technique for learning how editors title their news stories. The introduced technique models the problem as a discrete optimization task in a feature-rich space, where the global optimum can be found in polynomial time by means of dynamic programming. We train and test our model with an extensive corpus of financial news, and compare it against a number of baselines by using standard metrics from the document summarization domain, as well as some new metrics proposed in this work. The obtained results are very appealing and substantiate the soundness of the approach.
Original language | English |
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Title of host publication | Multilingual Text Analysis |
Subtitle of host publication | Challenges, Models, and Approaches |
Publisher | World Scientific Publishing Co. |
Pages | 201-243 |
Number of pages | 43 |
ISBN (Electronic) | 9789813274884 |
ISBN (Print) | 9789813274877 |
DOIs | |
State | Published - 1 Jan 2019 |
Externally published | Yes |
ASJC Scopus subject areas
- General Computer Science