Query-focused multi-document summarization: automatic data annotations and supervised learning approaches

Abstract

Abstract In this paper, we apply different supervised learning techniques to build query-focused multi-document summarization systems, where the task is to produce automatic summaries in response to a given query or specific information request stated by the user. A huge amount of labeled data is a prerequisite for supervised training. It is expensive and time-consuming when humans perform the labeling task manually. Automatic labeling can be a good remedy to this problem. We employ five different automatic annotation techniques to build extracts from human abstracts using ROUGE, Basic Element overlap, syntactic similarity measure, semantic similarity measure, and Extended String Subsequence Kernel. The supervised methods we use are Support Vector Machines, Conditional Random Fields, Hidden Markov Models, Maximum Entropy, and two ensemble-based approaches. During different experiments, we analyze the impact of automatic labeling methods on the performance of the applied supervised methods. To our knowledge, no other study has deeply investigated and compared the effects of using different automatic annotation techniques on different supervised learning approaches in the domain of query-focused multi-document summarization.