ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs
Abstract
In this paper, we investigate how to automatically extract attractive summaries for news propagation on microblogs and propose a novel system called ATSUM to achieve this goal via text attractiveness analysis. It first analyzes the sentences in a news article and automatically predict the attractiveness score of each sentence by using the support vector regression method. The predicted attractiveness scores are then incorporated into a summarization system. Experimental results on a manually labeled dataset verify the effectiveness of the proposed methods.
Cite
Text
Liu and Wan. "ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11076Markdown
[Liu and Wan. "ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/liu2017aaai-atsum/) doi:10.1609/AAAI.V31I1.11076BibTeX
@inproceedings{liu2017aaai-atsum,
title = {{ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs}},
author = {Liu, Fang and Wan, Xiaojun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4957-4958},
doi = {10.1609/AAAI.V31I1.11076},
url = {https://mlanthology.org/aaai/2017/liu2017aaai-atsum/}
}