Abstract:
"
Text summarization is one of the most important fields of NLP, which is in immense growth
since decades. Though there is a tremendous g4owth in text summary for the languages such
as English and Spanish in the languages with rich morphology such as Tamil, the research is
more diminutive. Though there are some models for Tamil text summarization, the generated
summary is not efficient as the features considered for the summary generation are statical
features. The main drawback of the existing models for the summary generation of Tamil texts
is that the semantics of words are not considered in the summary generation.
The approach considers the semantics of words when generating the summary by considering
the sentences with the critical sequence of words are the most important sentences. As the
sequence of words carries the meaning of words, the semantics will be considered in the
generation of summary, improving accuracy. So the readability of the generated summary will
be increased. The proposed approach suggests novel criteria to sort the sequences of words
according to their importance.
The consideration of semantics in the generation of summary increases the accuracy and
readability of the summary. "
