Multi-document Summarization by Ensembling of Scoring and Topic Modeling Techniques (2024)

Abstract: With the growing volume of text, finding relevant information is increasingly difficult. Automatic Text Summarization (ATS) addresses this by efficiently extracting relevant content from large document collections. Despite progress, ATS faces challenges like managing long, repetitive sentences, preserving coherence, and maintaining semantic alignment. This work introduces an extractive summarization approach based on topic modeling to address these issues. The proposed method produces summaries with representative sentences, reduced redundancy, concise content, and strong semantic consistency. Its effectiveness, demonstrated through experiments on DUC datasets, outperforms state-of-the-art techniques.

## introduction automatic text summarization (ats) condenses data into concise summaries @xcite , with research focusing on improved methodologies. key ats tasks include text understanding, content compression, summary representation, and relevant information identification @xcite . ats involves three stages: interpretation, transformation, and generation. challenges remain in identifying implicit information, avoiding redundancy, and maintaining readability. ats can be abstractive or extractive @xcite . abstractive summarization uses natural language generation, while extractive summarization involves text representation, relevance scoring, and key sentence selection. summaries can be generic or query-focused @xcite , and indicative or informative. topic modeling, a statistical method, enhances extractive summarization by clustering semantically similar words into topics, ensuring relevance and coherence @xcite . numerous studies have explored text summarization via topic modeling @xcite @xcite . however, challenges persist, including an optimal topic number for lda, limited diversity, sentence overlap, low representativeness, readability issues, long sentences, poor semantic relationship accounting, and key sentence omission. this study proposes a novel multi-document extractive summarization technique integrating topic modeling with unified scoring mechanisms and semantic sentence similarity, improving summary quality by addressing these challenges. the contributions of this paper are as follows: ## conclusion and future work this study proposes a method combining ldabased topic modeling with a unified scoring system for extractive multi-document summarization. it ensures concise summaries with high representativeness, minimal redundancy, and strong semantic similarity, evaluated using rouge scores on duc datasets. experimental results show its superiority figure 5: rouge score comparison (duc-2002) figure 6: rouge score comparison (duc-2006)