Topical sentence embedding for query focused document summarization

Distributed vector representation for sentences have been utilized in summarization area, since it simplifies semantic cosine calculation between sentence to sentence as well as sentence to document. Many extension works have been done to incorporate latent topics and word embedding, however, few of them assign sentences with explicit topics. Besides, much sentence embedding framework follows the same spirit of prediction task about a word in the sentence, which omits the sentence-to-sentence coherence. To address these problems, we proposed a novel sentence embedding framework to collaborate the current sentence representation, word-based content and topic assignment of the sentence to predict the next sentence representation. The experiments on summarization tasks show our model outperforms state-of-the-art methods.