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Authors: Liqun Shao ; Hao Zhang ; Ming Jia and Jie Wang

Affiliation: University of Massachusetts, United States

Keyword(s): Single-Document Summarizations, Keyword Ranking, Topic Clustering, Word Embedding, SoftPlus Function, Semantic Similarity, Summarization Evaluation, Realtime.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Context Discovery ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems

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. 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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Paper citation in several formats:
Shao, L.; Zhang, H.; Jia, M. and Wang, J. (2017). Efficient and Effective Single-Document Summarizations and a Word-Embedding Measurement of Quality. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR; ISBN 978-989-758-271-4; ISSN 2184-3228, SciTePress, pages 114-122. DOI: 10.5220/0006581301140122

@conference{kdir17,
author={Liqun Shao. and Hao Zhang. and Ming Jia. and Jie Wang.},
title={Efficient and Effective Single-Document Summarizations and a Word-Embedding Measurement of Quality},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR},
year={2017},
pages={114-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006581301140122},
isbn={978-989-758-271-4},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR
TI - Efficient and Effective Single-Document Summarizations and a Word-Embedding Measurement of Quality
SN - 978-989-758-271-4
IS - 2184-3228
AU - Shao, L.
AU - Zhang, H.
AU - Jia, M.
AU - Wang, J.
PY - 2017
SP - 114
EP - 122
DO - 10.5220/0006581301140122
PB - SciTePress