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Generic text summarization using probabilistic latent semantic indexing (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bhandari, Harendra Ito, Takahiko Shimbo, Masashi Matsumoto, Yuji |
| Description | 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. 1 In Proceedings of IJCNLP |
| File Format | |
| Language | English |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Single One Extractive Summary Graph Structure Probabilistic Latent Semantic Indexing Topic Detection Topic Spectrum Generic Summary Effective Method Term Frequency Generic Text Summarization Overall Idea Several Topic Original Document Human Being Summary Containing Sentence |
| Content Type | Text |
| Resource Type | Article |
