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arXiv · 1710.00284

Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality

Liqun Shao·Hao Zhang·Ming Jia·Jie Wang
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Abstract

Our task is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate the quality of summaries without human-generated benchmarks, we define a measure called WESM based on word-embedding using Word Mover's Distance. We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.

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Liqun Shao, Hao Zhang, Ming Jia, Jie Wang. 2017-10-01. Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality. https://arxiv.org/abs/1710.00284

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