Headline Generation: Learning from Decomposable Document Titles

Oleg Vasilyev; Tom Grek; John Bohannon

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arXiv · 1904.08455

Headline Generation: Learning from Decomposable Document Titles

Oleg Vasilyev·Tom Grek·John Bohannon

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Abstract

We propose a novel method for generating titles for unstructured text documents. We reframe the problem as a sequential question-answering task. A deep neural network is trained on document-title pairs with decomposable titles, meaning that the vocabulary of the title is a subset of the vocabulary of the document. To train the model we use a corpus of millions of publicly available document-title pairs: news articles and headlines. We present the results of a randomized double-blind trial in which subjects were unaware of which titles were human or machine-generated. When trained on approximately 1.5 million news articles, the model generates headlines that humans judge to be as good or better than the original human-written headlines in the majority of cases.

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Oleg Vasilyev, Tom Grek, John Bohannon. 2019-04-17. Headline Generation: Learning from Decomposable Document Titles. https://arxiv.org/abs/1904.08455

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