Multi-Document Summarization Techniques: A Comparative Study

Text summarization is the process of generating a shorter version of a longer text while retaining its most important information. The aim of text summarization is to extract the most important information from a document and present it in a concise and easy-to-read format. There are two main types of text summarization: extractive and abstractive. Multi-document summarization is a text summarization technique that aims to generate a summary from multiple documents instead of just one. There are various algorithms for implementing Multi-document summarization such as TextRank, LexRank, Centroid Based Summarization, Maximal Marginal Relevance, Cluster-based Sentence Selection (CBSS), SumBasic and K-Means Clustering. In this paper, we present an overview of Multi-document summarization and five methods: TextRank, LexRank, Centroid Based Summarization, Maximal Marginal Relevance and K-Means Clustering. Also we have discussed how these algorithms can be evaluated using several metrics such as Precision, Recall, F1 Score, Pyramid Score, Content Selection Score, Sentence Compression Ratio.