Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries

Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng

Abstract: We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information needs. We rank and extract the utterances in a conversation based on the overall content and the phrasal query information. We cluster the selected sentences based on their lexical similarity and aggregate the sentences in each cluster by means of a word graph model. We propose a ranking strategy to select the best path in the constructed graph as a query-based abstract sentence for each cluster. A resulting summary consists of abstractive sentences representing the phrasal query information and the overall content of the conversation. Automatic and manual evaluation results over meeting, chat and email conversations show that our approach significantly outperforms baselines and previous extractive models.