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@article{198188,
        author = {V. B. Mhaske and D.S. Jadhav},
        title = {E-commerce Product Review Text Summarization Using Natural Language Toolkit},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9644-9649},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198188},
        abstract = {In recent years, the volume of textual data has grown rapidly as people increasingly rely on written communication to share their ideas and opinions, especially through e-commerce product reviews. Most online shopping platforms enable customers to rate products and submit written feedback. These reviews play a crucial role by offering organizations valuable information about product performance while also helping prospective buyers develop opinions and make well-informed purchasing decisions.
Conventional product evaluation methods mainly depend on numerical ratings to judge quality. However, this study introduces an alternative approach that focuses on analyzing review texts through text summarization techniques using the Natural Language Toolkit (NLTK). The primary aim of this research is to derive meaningful insights from large collections of customer reviews to support better decision-making. Text summarization can function as an effective decision-support mechanism, assisting consumers in determining whether to purchase a product and enabling companies to gain deeper, more actionable feedback about their products.},
        keywords = {Comparison, NLTK, Reviews, Similarity score, Text summarization.},
        month = {April},
        }