An Abstract Argumentation-Based Approach to Automatic Extractive Text Summarization

Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content. Providing effective solutions to this task would allow the users of Digital Libraries to save time in selecting documents that may be appropriate for satisfying their information needs or for supporting their decision-making tasks. This paper proposes an approach, based on abstract argumentation, to select the sentences in a text that are to be included in its summary. The proposed approach obtained interesting experimental results on the English subset of the benchmark MultiLing 2015 dataset.