Learning to Rank for Query-Focused Multi-document Summarization

In this paper, we explore how to use ranking SVM to train the feature weights for query-focused multi-document summarization. To apply a supervised learning method to sentence extraction in multi-document summarization, we need to derive the sentence labels for training corpus from the existing human labeling data in form of. However, this process is not trivial, because the human summaries are abstractive, and do not necessarily well match the sentences in the documents. In this paper, we try to address the above problem from the following two aspects. First, we make use of sentence-to-sentence relationships to better estimate the probability of a sentence in the document set to be a summary sentence. Second, to make the derived training data less sensitive, we adopt a cost sensitive loss in the ranking SVM's objective function. The experimental results demonstrate the effectiveness of our proposed method.