  Automatic chat summarization can help people quickly grasp important
information from numerous chat messages. Unlike conventional documents, chat
logs usually have fragmented and evolving topics. In addition, these logs
contain a quantity of elliptical and interrogative sentences, which make the
chat summarization highly context dependent. In this work, we propose a novel
unsupervised framework called RankAE to perform chat summarization without
employing manually labeled data. RankAE consists of a topic-oriented ranking
strategy that selects topic utterances according to centrality and diversity
simultaneously, as well as a denoising auto-encoder that is carefully designed
to generate succinct but context-informative summaries based on the selected
utterances. To evaluate the proposed method, we collect a large-scale dataset
of chat logs from a customer service environment and build an annotated set
only for model evaluation. Experimental results show that RankAE significantly
outperforms other unsupervised methods and is able to generate high-quality
summaries in terms of relevance and topic coverage.
