Survey on NLP based automatic extractive text summarization using spacy
Bibliographic Data
Automatic abstraction of text is considered one of the most difficult problems because mathematically there is no real way to test a summary but one can distinguish between the appropriate summary. Also, abbreviations may be of a variety of abstractive forms in which new phrases and terms are used, in contrast Extractive form in which scoring sentences from enter textual content gets extracted as a summary sentence. The growing discovery of online facts has necessitated extensive research within the region of automated text within the Natural language processing (NLP). Over the course of the year, researchers worked on them in many domains. Therefore, the survey plans to look at the number of commonly used processes within single-line and multi-line summaries, providing unique emphasis on real-world approaches and clear strategies. Some well-known techniques that focus on specific information of the default summary are mentioned
Abstraction · Automatic summarization · Contrast (vision · Focus (optics · Information retrieval · Natural language processing · Relevance (law · Sentence · Variety (cybernetics · Advanced Text Analysis Techniques · Computer Science · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |