PHD Discussions Logo

Ask, Learn and Accelerate in your PhD Research

Question Icon Post Your Answer

Question Icon

How can feature selection be incorporated into Latent Semantic Analysis (LSA) for text summarization?

What is the role of feature selection in enhancing LSA-based text summarization?

All Answers (1 Answers In All)

By Reema Answered 1 year ago

Feature selection in LSA is implemented during preprocessing, before the term-document matrix is built, to reduce noise and dimensionality. This involves lexical filtering (removing stop words, applying stemming/lemmatization), frequency-based methods to trim rare and ubiquitous terms, and statistical filtering like selecting top-N terms by TF-IDF weight. The result is a cleaner latent semantic space, which improves the interpretability of topics and the coherence of extracted summaries.

Your Answer