loading
Papers

Research.Publish.Connect.

Paper

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

Affiliation: University of Massachusetts, United States

ISBN: 978-989-758-271-4

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.217.40

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 1: KDIR, ISBN 978-989-758-271-4, 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 - Volume 1: KDIR,},
year={2017},
pages={114-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006581301140122},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Efficient and Effective Single-Document Summarizations and a Word-Embedding Measurement of Quality
SN - 978-989-758-271-4
AU - Shao, L.
AU - Zhang, H.
AU - Jia, M.
AU - Wang, J.
PY - 2017
SP - 114
EP - 122
DO - 10.5220/0006581301140122

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.