Malayalam text summarization: Minimum spanning tree based graph reduction approach

Raj, M Rahul; Haroon, Rosna P

doi:10.1109/icaccaf.2016.7748995

Cited by 6 publications

(1 citation statement)

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“…A graph-based method for Malayalam documents has been proposed in [11] where the sentences are represented as nodes and vertex weight is calculated using similarity measures. Minimum spanning tree Malayalam summarizer [12] creates a semantic graph from the input document and thereby graph reduction is performed using minimum spanning tree concept by creating repetitive subgraphs.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Machine Learning-based Text Summarization in the Malayalam Language

2022

KSII TIIS

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Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.

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Section: Related Workmentioning

confidence: 99%