IIIT Hyderabad Publications
Non-decreasing Sub-modular Function for Comprehensible Summarization
Authors: Litton J Kurisinkel,Pruthwik Mishra,Vigneshwaran Muralidharan,Vasudeva Varma,Dipti Misra Sharma
Conference: The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics
Location San Diego, California
Report no: IIIT/TR/2016/38
Extractive summarization techniques typically aim to maximize the information coverage of the summary with respect to the original corpus and report accuracies in ROUGE scores. Automated text summarization techniques should consider the dimensions of comprehensibility, coherence and readability. In the current work, we identify the discourse structure which provides the context for the creation of a sentence. We leverage the information from the structure to frame a monotone (non-decreasing) sub-modular scoring function for generating comprehensible summaries. Our approach improves the overall quality of comprehensibility of the summary in terms of human evaluation and gives sufficient content coverage with comparable ROUGE score. We also formulate a metric to measure summary comprehensibility in terms of Contextual Independence of a sentence. The metric is shown to be representative of human judgement of text comprehensibility.
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Centre for Language Technologies Research Centre
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