Topic modeling can be used in content summarization to help identify and extract the most important and relevant information from a large body of text.
Here’s how topic modeling is employed in content summarization:
- Topic Identification: Topic modeling techniques like Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) are applied to the text corpus to identify the main topics or themes present in the content. These topics represent the key subject areas or concepts covered in the text.
- Document-Topic Assignment: Each document or section of the text is assigned a distribution over the identified topics. This distribution indicates the degree to which each topic is present in the document. For example, a news article about technology may have a high topic distribution for “technology” and a lower distribution for “politics.
- Sentence Scoring: After topic assignment, the sentences within each document are scored based on their relevance to the dominant topics. Sentences that contain keywords or phrases associated with the dominant topics are given higher scores.
- Sentence Selection: The sentences with the highest scores are selected for inclusion in the summary. These sentences are deemed to contain the most important information related to the main topics.
- Summarization Generation: The selected sentences are then assembled to create a coherent and concise summary of the content. This summary provides a condensed version of the original text, focusing on the primary topics and key points.
- Abstractive Summarization (Optional): In some cases, abstractive summarization techniques, which involve generating summary sentences rather than selecting existing sentences, can be combined with topic modeling to create more human-like summaries.