Using Latent Semantic Analysis in Text Summarization and Summary Evaluation

This paper deals with using latent semantic analysis in text summarization. We describe a generic text summarization method which uses the latent semantic analysis technique to identify semantically important sentences. This method has been further improved. Then we propose two new evaluation methods based on LSA, which measure content similarity between an original document and its summary. In the evaluation part we compare seven summarizers by a classical content-based evaluator and by the two new LSA evaluators. We also study an influence of summary length on its quality from the angle of the three mentioned evaluation methods.