Text Summarization Using NLP and Fuzzy Logic Based ML Techniques
DOI:
https://doi.org/10.63949/Keywords:
- Text Summarization,
- NLP,
- Machine Learning,
- Fuzzy systems
Abstract
In today world, there are large amount of content that are available digitally and extracting information from them become more challenging for today’s situation. However, automated summarization plays major role in extracting features from the data. Traditional summarization method have certain limitations such as lack of multiple language support. This paper proposes the advanced framework to summarize news text effectively using Natural Language Processing and Machine Learning based Techniques. With help of NLP, we can extract future insights and advanced features that help us understand the content more precisely. With help of fuzzy logic scoring, we can score each sentence effectively based on certain model and extract important sentence that are required for Summarization. As said earlier, ML based scoring also include evaluating certain concepts like term frequency and inverse document frequency (TF-IDF) and similarity in semantics to evaluate the sentence. This current study combines ML based fuzzy scoring and advanced NLP techniques to summarize the news data.
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References
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