@inproceedings{5e17f9882c524eab9f4048e6fc57b9a3,
title = "HEADS: Headline generation as sequence prediction using an abstract: Feature-rich space",
abstract = "Automatic headline generation is a sub-task of document summarization with many reported applications. In this study 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. In this space the global optimum can be found in polynomial time by means of dynamic programming. We train and test our model on 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 ones proposed in this work. We also assess the readability and informativeness of the generated titles through human evaluation. The obtained results are very appealing and substantiate the soundness of the approach.",
author = "Colmenares, \{Carlos A.\} and Marina Litvak and Amin Mantrach and Fabrizio Silvestri",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 ; Conference date: 31-05-2015 Through 05-06-2015",
year = "2015",
month = jan,
day = "1",
doi = "10.3115/v1/n15-1014",
language = "English",
series = "NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "133--142",
booktitle = "NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics",
address = "United States",
}