@inproceedings{eb156123cf724d009841f4bdbfb63b7e,
title = "Summarizing weibo with topics compression",
abstract = "Extractive text summarization aims at selecting a small subset of sentences so that the contents and meaning of the original document are best preserved. In this paper we describe an unsupervised approach to extractive summarization. It combines hierarchical topic modeling (TM) with the Minimal Description Length (MDL) principle and applies them to Chinese language. Our summarizer strives to extract information that provides the best description of text topics in terms of MDL. This model is applied to the NLPCC 2015 Shared Task of Weibo-Oriented Chinese News Summarization [1], where Chinese texts from news articles were summarized with the goal of creating short meaningful messages for Weibo (Sina Weibo is a Chinese microblogging website, one of the most popular sites in China.) [2]. The experimental results disclose superiority of our approach over other summarizers from the NLPCC 2015 competition.",
author = "Marina Litvak and Natalia Vanetik and Lei Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 ; Conference date: 17-04-2017 Through 23-04-2017",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-77116-8_39",
language = "English",
isbn = "9783319771151",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "522--534",
editor = "Alexander Gelbukh",
booktitle = "Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers",
address = "Germany",
}