Topic-based automatic summarization algorithm for Chinese short text | JoVE Visualize

Area of Science:

Natural Language Processing
Computational Linguistics
Information Retrieval

Background:

Current automatic summarization methods primarily focus on English, facing challenges with Chinese text due to complex syntax, word distinctions, and polysemy.
Existing Chinese text summarization often results in incoherent summaries by merely selecting and arranging important sentences.
Chinese short texts, like those on social media, frequently contain redundant information and lack neat sentence structures, complicating summarization.

Purpose of the Study:

To develop an effective topic-based automatic summarization method specifically for Chinese short texts.
To address the limitations of existing methods in handling the complexities of Chinese language and short text formats.
To improve the conciseness, richness, and readability of summaries for applications like disaster relief and information dissemination.

Main Methods:

A key sentence selection approach combining topic words and TF-IDF (Term Frequency-Inverse Document Frequency) to score sentences against topics.
Identification of the highest-scoring sentence as the topic sentence for each topic.
Implementation of three post-processing "retouching" mechanisms to refine the extracted topic sentences for improved quality.

Main Results:

The proposed method successfully generates polished topic summaries that accurately reflect the relationship between topics and events (natural disasters, social hot events).
Summaries exhibit rich semantic information and capture essential elements of the discussed topics.
Experimental validation on Sina Weibo datasets demonstrates the effectiveness of the approach.

Conclusions:

The topic-based summarization method significantly improves the quality and utility of automatic summaries for Chinese short texts.
The approach provides a robust solution for extracting key information from noisy social media data.
This method has practical implications for efficient information retrieval in critical situations and general user needs.