Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Automated summary

Significant advances have been made in the field of text summarization, with a focus on news summarization. To create summaries of various news articles in the context of erroneous online data, it is essential to develop a synthesis approach that can extract, compare, and rank sentences. It is also necessary for the news summarization system to handle multi-document summaries due to content redundancy. This paper proposes a method for summarizing multi-document news web pages using similarity models and sentence ranking, which outperforms other recent methods in summarizing news articles according to experimental results.