# Headline Generation: Learning from Decomposable Document Titles

arXiv: https://arxiv.org/abs/1904.08455 · HTML: https://mlsanity.com/paper/1904.08455/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation](https://mlsanity.com/paper/2103.10918/) (2021)
- [DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines](https://mlsanity.com/paper/2107.10935/) (2021)

## Abstract (arXiv)

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.