  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.
