পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মাল্টি-ডকুমেন্ট সামারাইজেশন× | TF-IDF× | |
|---|---|---|
| ক্ষেত্র | টেক্সট খনন | টেক্সট খনন |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | — | 1988 |
| প্রবর্তক≠ | — | Salton & Buckley |
| ধরন≠ | NLP text-summarization task | Text vectorization / term-weighting scheme |
| মৌলিক উৎস≠ | Erkan, G. & Radev, D.R. (2004). LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479. link ↗ | Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. DOI ↗ |
| অপর নাম | MDS, Çok Belgeli Özetleme (Multi-Document Summarization), multi-source summarization | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| সম্পর্কিত≠ | 5 | 3 |
| সারসংক্ষেপ≠ | Multi-document summarization (MDS) is a natural-language-processing task that condenses a cluster of related documents into a single comprehensive, coherent, and non-redundant summary. Formally described by Erkan and Radev (2004) through the LexRank algorithm, MDS is used in news cluster analysis, systematic literature reviews, and research synthesis to give readers a unified view of information spread across multiple sources. | TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere. |
| ScholarGateডেটাসেট ↗ |
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