手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 複数文書要約× | トピックモデリング× | |
|---|---|---|
| 分野≠ | テキストマイニング | 深層学習 |
| 系統≠ | Process / pipeline | Machine learning |
| 提唱年≠ | — | 1999–2003 |
| 提唱者≠ | — | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| 種類≠ | NLP text-summarization task | Unsupervised generative probabilistic model |
| 原典≠ | 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 ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| 別名≠ | MDS, Çok Belgeli Özetleme (Multi-Document Summarization), multi-source summarization | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| 関連 | 5 | 5 |
| 概要≠ | 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. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
| ScholarGateデータセット ↗ |
|
|