This paper presents a strategy to generate generic summary of documents using Probabilistic Latent Semantic Indexing. Generally a document contains several topics rather than a single one. Summaries created by human beings tend to cover several topics to give the readers an overall idea about the original document. Hence we can expect that a summary containing sentences from better part of the topic spectrum should make a better summary. PLSI has proven to be an effective method in topic detection. In this paper we present a method for creating extractive summary of the document by using PLSI to analyze the features of document such as term frequency and graph structure. We also show our results, which was evaluated using ROUGE, and compare the results with other techniques, proposed in the past.
Another work, text summarization using Probabilistic Latent Semantic Indexing (PLSI) REF , proposed an algorithm that extracts more than one topic from a document by analyzing surface features such as term frequency and graph structure for a single document.
Generic Text Summarization Using Probabilistic Latent Semantic Indexing
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science