收稿日期: 2014-07-24
修回日期: 2014-08-22
网络出版日期: 2014-09-20
基金资助
本文系国家社会科学基金重大招标项目“面向学科领域的网络信息资源深度聚合与服务研究”（项目编号：12&ZD221）和国家自然科学基金项目“融合范式视角下的链接分析理论集成框架及其实证研究”（项目编号：71273125）研究成果之一。
A Review on Machine Learning Oriented Automatic Summarization
Received date: 2014-07-24
Revised date: 2014-08-22
Online published: 2014-09-20
曹洋 , 成颖 , 裴雷 . 基于机器学习的自动文摘研究综述[J]. 图书情报工作, 2014 , 58(18) : 122 -130 . DOI: 10.13266/j.issn.0252-3116.2014.18.018
This paper probes into the process of automatic summarization based on machine learning, including features selection, algorithm selection, model training, abstracts extraction, model evaluation. The Review focuses on three main machine learning algorithms: Naive Bayes, Hidden Markov Model and Conditional Random Fields, mainly elaborating the idea of these algorithms, summarizing related research, and giving reflections. Then it discusses the common problems with three machine learning algorithms, including training methods, collaborative training and active learning, category balance, terms distribution. In the end, future research directions are explored.
Key words： automatic summarization; machine learning; NB; HMM; CRF
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