This research improves the presentation of an automatic multiple-document summarization system, which produces the frequently asked questions (FAQs) of a topical forum. The design and findings of the proposed presentation structure based on a four-part pattern of traditional Chinese articles are presented. An experiment was designed to conduct an objective experimental analysis based on criteria consisting of compression rate, recall rate, and precision rate, as well as a subjective experimental analysis based on user acceptance in terms of the indication, readability, appropriate number of sentences, and structure of the summary. The experimental results show that the proposed summary presentation structure with both domain-terminology corpus methods produced a significantly improved summary presentation compared with the original system. Nevertheless, the FAQ summary presentation system could have used complete sentences instead of partial sentences from the original articles for improved readability.

https://ir.nuk.edu.tw/bitstream/310360000Q/10001/2/ESwA4888Online.pdf

Summary of FAQs from a topical forum based on the native composition structure

—In recent times, there has been a significant increase in interest towards automatic text summarization in the field of natural language processing. The primary objective of text summarization is to provide a condensed version of a lengthy text that contains important information, enabling users to quickly understand the main content of the document without having to read through the entire text. This research paper presents an all-inclusive overview of the latest advancements in text summarization techniques. The different methods for text summarization, including extraction- based, abstraction-based, and hybrid approaches, are discussed. Additionally, the paper covers the various evaluation metrics that are utilized to determine the effectiveness of text summarizers, along with the datasets that are commonly used for training and testing purposes. In addition, we present a comprehensive survey of state-of-the- art text summarization models, including deep learning-based approaches such as transformers and graph-based models. We also discuss the challenges and open problems in text summarization, such as generating coherent and readable summaries that capture the essence of the original text. Finally, we conclude by discussing potential applications of text summarization, such as in news articles, scientific papers, and social media posts. We hope that this paper provides a useful resource for researchers and practitioners interested in text summarization and its applications.

Summarization Of Text Using Natural Language Processing

An automatic multiple-document summarization system for producing frequently asked questions (FAQ) of a topical forum can save forum Webmasters a great deal of time in theory. This work will address summary composition issue of a previous work by proposing a structured presentation based on a four-part pattern of traditional Chinese articles. The result of the experiment shows that the enhanced system with both domain-terminology corpus methods produced a significantly better summary presentation than the original system. Recall rate and precision rate performance indices and user evaluations are also presented and discussed to show their practical implications.

Summary of a Topical Forum FAQ Based on the Chinese Composition Structure

We propose a text summarization approach that clusters text units before extracting summary sentences. Text units are formed by combining sentences based on rhetorical structure information. The rhetorical structure information we use is the one immediately recognizable at the surface level, making this approach language independent as much as possible. Experiments conducted with both Korean and English text collections show that the approach improves the quality of summarization.

Sung Hyon Myaeng

Papers

Text Summarization Based on Sentence Clustering with Rhetorical Structure Information