Generating Digests From Educational Articles Automatically Based on Second Order HMM

DOI

10.2991/icitel-15.2016.28How to use a DOI?

Keywords

Summary generating; HMM; word segmentation

Abstract

Automatically generating summary of articles is very important when we encounter explosive reading information; computers can help people on text compression, extraction, representation and obtain core text content automatically. However, computer still encounters a lot of difficulties, for example, how to divide words from ambiguity, inaccuracies, redundancy of the lengthy article, and so on. This paper presents an improved Hidden Markov Model (HMM) Word segmentation method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Canli Wu
AU  - Lican Huang
PY  - 2016/03
DA  - 2016/03
TI  - Generating Digests From Educational Articles Automatically Based on Second Order HMM
BT  - Proceedings of the 1st International Conference on Information Technologies in Education and Learning
PB  - Atlantis Press
SP  - 122
EP  - 124
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitel-15.2016.28
DO  - 10.2991/icitel-15.2016.28
ID  - Wu2016/03
ER  -