SummaryLSA

SummaryLSA is an approach to automatic text summarization that applies latent semantic analysis to identify sentences that best represent the content of a document or collection of documents. It is typically used for extractive summarization, selecting existing sentences rather than generating new text, with the goal of preserving main topics while reducing redundancy.

The method builds a term-sentence matrix from the input text, often using weighting schemes such as tf-idf. It then applies singular value decomposition to obtain a low-dimensional latent space that captures underlying topics. Each sentence is scored by its contribution to the top latent components, and sentences with the highest significance are selected to form the summary. Redundancy reduction steps are commonly employed to avoid repeated information, sometimes by checking similarity between candidate sentences.

Variants and extensions of SummaryLSA include different choices for weighting, the number of latent components, and the sentence selection strategy. For multi-document summaries, the matrix can incorporate sentences from multiple sources, and diversity constraints can help ensure coverage of distinct topics without duplication.

Applications of SummaryLSA span general document summarization, information retrieval support, and rapid overview of large textual collections. It is valued for being computationally efficient and interpretable compared to more data-hungry neural methods, and it typically requires less large-scale training data.

Limitations include sensitivity to vocabulary and preprocessing, potential for incoherent or incomplete narratives, and a tendency to produce extractive rather than abstractive summaries. As a result, SummaryLSA is often seen as a traditional baseline or complementary approach rather than a state-of-the-art solution for modern abstractive summarization.