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.