Text Summarization is about creating a concise summary of a longer text to capture its main ideas. It can be likened to crafting brief notes for efficient review before an exam, condensing comprehensive information into a succinct format. In essence, it generates an accurate summary of a more extensive text.
Text Summarization can be categorized into two main types:
- Extractive Summarization: Extractive Summarization involves selecting and extracting the most vital sentences or phrases directly from the source text. The selection is based on relevance and cohesiveness in representing the key points of the original text. Examples include summarizing news articles, legal documents, and research papers.
- Abstractive Summarization: Abstractive Summarization goes beyond extraction by generating novel sentences not present in the original text. This process requires a deeper analysis of the content and can be applied to create concise summaries of medical reports, business documents, social media posts, and user-generated content.
- Text Preprocessing: This step involves data cleaning, including lowercasing, removing special characters, and eliminating stop words.
- Tokenization: The text is fragmented into sentences (sentence tokenization) and further divided into words (word tokenization) for analysis.
- Word Frequency Analysis: A word-frequency table is created to determine the importance of words in the document.
- Scoring and Ranking: Sentences or words are assigned scores based on criteria like word frequency, TF-IDF, and sentence position.
- Sentence Generation (for Abstractive): Depending on the summarization type, either the highest-scoring sentences are selected or new sentences are generated to form a summary.
- Language Generation (for Abstractive): In abstractive summarization, the generated sentences are ensured to be grammatically correct and coherent.
- Summary Composition: The chosen or generated sentences are assembled into a concise and informative summary.