Single Document Text Summarization of a Resource-Poor Language using an Unsupervised Technique

Abstract

Automatic text summarization of a resource-poor language is a challenging task. Unsupervised extractive techniques are often preferred for such languages due to scarcity of resources. Latent Semantic Analysis (LSA) is an unsupervised technique which automatically identifies semantically important sentences from a text document. Two methods based on Latent Semantic Analysis have been evaluated on two datasets of a resource-poor language using Singular Value Decomposition (SVD) on different vector-space models. The performance of the methods is evaluated using ROUGE-L scores obtained by comparing the system generated summaries with human generated model summaries. Both the methods are found to be performing better for shorter documents than longer ones.