विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बहु-दस्तावेज़ संक्षेपण× | पाठ वर्गीकरण× | |
|---|---|---|
| क्षेत्र | पाठ खनन | पाठ खनन |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष | — | — |
| प्रवर्तक | — | — |
| प्रकार≠ | NLP text-summarization task | Supervised NLP classification task |
| मौलिक स्रोत≠ | 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 ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| उपनाम≠ | MDS, Çok Belgeli Özetleme (Multi-Document Summarization), multi-source summarization | text categorization, document classification, topic classification, metin sınıflandırma |
| संबंधित≠ | 5 | 4 |
| सारांश≠ | 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. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGateडेटासेट ↗ |
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