  Neural models have recently been used in text summarization including
headline generation. The model can be trained using a set of document-headline
pairs. However, the model does not explicitly consider topical similarities and
differences of documents. We suggest to categorizing documents into various
topics so that documents within the same topic are similar in content and share
similar summarization patterns. Taking advantage of topic information of
documents, we propose topic sensitive neural headline generation model. Our
model can generate more accurate summaries guided by document topics. We test
our model on LCSTS dataset, and experiments show that our method outperforms
other baselines on each topic and achieves the state-of-art performance.
