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Automatic content curation of news events
Hei-Chia Wang1,2, Chun-Chieh Chen1,3, Ting-Wei Li1
1Institute of Information Management, National Cheng Kung University, Tainan, Taiwan.
Multimedia Tools and Applications
|February 23, 2022
Summary
This study introduces an automated news curation method to help readers quickly understand developing events. It uses topic modeling and a hidden Markov model to generate chronological summaries at key time points.
Area of Science:
- Natural Language Processing
- Information Retrieval
- Computational Linguistics
Background:
- Online news presents vast information, making it time-consuming for readers to track event developments and context.
- Manual news aggregation and summarization are inefficient for keeping pace with evolving online news.
- Existing methods often lack chronological context, hindering a comprehensive understanding of news events.
Discussion:
- The proposed automated news curation method leverages topic extraction and hidden Markov models to identify topic evolution.
- It analyzes topic strength and variation to pinpoint critical time points in news event development.
- Concise, chronologically ordered summaries are generated at these key junctures.
Key Insights:
- The system effectively automates the process of news curation, reducing manual effort.
- Accurate detection of significant event phases and generation of relevant news summaries were demonstrated.
- The integration of chronological information and summarization enhances reader comprehension of news event context.
Outlook:
- Further research could explore incorporating multimedia elements into curated news.
- Expanding the model to handle multilingual news sources presents a future direction.
- Investigating user engagement with automatically curated news timelines could provide valuable feedback.
Keywords:
Automatic news summaryContent curationDocument summarizationEvent storyNews eventTopic detectionMore Related Videos
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