Implementation of NLP based automatic text summarization using spacy

Bibliographic Data

ID15798237
AuthorsNayana Cholanayakanahalli Prakash Prakash (ESI - Post Graduate Institute of Medical Science and Research, corresponding author), Achyutha Prasad N (0000-0002-2356-4146, ESI - Post Graduate Institute of Medical Science and Research, corresponding author), Achyutha Prasad Narasimhaiah, Jagadeesh Bettakote Nagaraj (ESI - Post Graduate Institute of Medical Science and Research, corresponding author), Piyush Kumar Pareek (0000-0003-2287-0122, Meenakshi Academy of Higher Education and Research, corresponding author), Nalini Bpalya Maruthikumar (ESI - Post Graduate Institute of Medical Science and Research, corresponding author), Ramya Iyaravally Manjunath (ESI - Post Graduate Institute of Medical Science and Research, corresponding author)
Year2022
Pages7508-7521
Publication date2022-08-09
Open AccessYes
TypeARTICLE
VenueInternational Journal of Health Sciences (JOURNAL)
Journal identifiersISSN: 2550-696X • E-ISSN: 2550-6978
PublisherUniversidad Tecnica de Manabi (PUBLISHER • EC)
DOI10.53730/ijhs.v6ns5.10574
OpenAlexW4290730915
LanguageEN
Citations received2
References cited6

The amount of data on the Internet has increased exponentially over the past decade. Therefore, we need a solution that converts this massive amount of raw information into useful information that the human brain can understand. One such common technique in research that helps when dealing with large amounts of data is text summarization. Automatic summarization is a well-known approach to reduce documents to key ideas. This works by storing important information by creating a shortened version of the text. Text summaries are divided into extraction and abstraction methods. The extraction summary method minimizes the summarization burden by selecting a subset of relevant sentences from the actual text. There are many methods, but researchers specializing in natural language processing (NLP) are particularly attracted to the extraction method. The meaning of the sentence is calculated using linguistic and statistical features. In this work, extractive and abstract methods for summarizing texts were examined. This white paper uses a spacey algorithm to analyze the above methods, resulting in fewer iterations and a more focused summary

Abstraction · Automatic summarization · Information extraction · Information retrieval · Key (lock · Meaning (existential · Natural language processing · Semantics (computer science · Sentence · Text graph · The Internet · World Wide Web · Advanced Text Analysis Techniques · Computer Science · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence

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