Abstractive text summarizationĪbstractive text summarization generates legible sentences from the entirety of the text provided. Owing to its simplicity in most use cases, extractive text summarization is the most common method used by automatic text summarizers. This, however, also means that the method is limited to predetermined parameters that can make extracted text biased under certain conditions. The method is very straightforward as it extracts texts based on parameters such as the text to be summarized, the most important sentences ( Top K), and the value of each of these sentences to the overall subject. Extractive text summarizationĪs the name suggests, extractive text summarization ‘extracts’ notable information from the large dumps of text provided and groups them into clear and concise summaries. ‘Extractive’ and ‘Abstractive’ are the two methods of performing text summarization. With this in mind, let’s first look at the two distinctive methods of text summarization, followed by five techniques that can be used in Python. This can get frustrating, especially during research and when collecting valid information for whatever reason. We’ve all come across articles and other long-form texts with a lot of unnecessary content that completely draws us away from the subject matter. Text summarization is a natural language processing (NLP) task that allows users to summarize large amounts of text for quick consumption without losing any important information.
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