Topic-Focused Multi-Document Summarization System
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A Topic-Focused Multi-Document Summarization System is a multi-document summarization system that generates topic-focused summaries from multiple documents based on specific topics or user queries.
- AKA: Query-Focused Multi-Document Summarization System, Topic-Based Multi-Document System, Focused Multi-Document System.
- Context:
- It can typically process Topic-Focused System Document Collections guided by topic-focused system topic specifications.
- It can typically implement Topic-Focused System Algorithms for topic-focused system content selection.
- It can typically compute Topic-Focused System Relevance Scores using topic-focused system similarity metrics.
- It can typically generate Topic-Focused System Summary Outputs addressing topic-focused system information needs.
- It can typically maintain Topic-Focused System Coherence while ensuring topic-focused system topic coverage.
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- It can often employ Topic-Focused System Components including topic analyzers and relevance filters.
- It can often utilize Topic-Focused System Resources such as topic models and query processors.
- It can often apply Topic-Focused System Optimization for topic-focused system performance tuning.
- It can often support Topic-Focused System Evaluation through topic-focused system benchmarks.
- ...
- It can range from being a Simple Topic-Focused Multi-Document Summarization System to being a Complex Topic-Focused Multi-Document Summarization System, depending on its topic-focused system complexity.
- It can range from being an Extractive Topic-Focused Multi-Document Summarization System to being an Abstractive Topic-Focused Multi-Document Summarization System, depending on its topic-focused system generation method.
- It can range from being a Single-Topic System to being a Multi-Topic System, depending on its topic-focused system topic handling.
- It can range from being a Research Topic-Focused Multi-Document Summarization System to being a Production Topic-Focused Multi-Document Summarization System, depending on its topic-focused system deployment maturity.
- It can range from being a Domain-General Topic-Focused Multi-Document Summarization System to being a Domain-Specific Topic-Focused Multi-Document Summarization System, depending on its topic-focused system specialization.
- It can range from being a Monolingual Topic-Focused Multi-Document Summarization System to being a Cross-Lingual Topic-Focused Multi-Document Summarization System, depending on its topic-focused system language capability.
- It can range from being a Static Topic-Focused Multi-Document Summarization System to being an Adaptive Topic-Focused Multi-Document Summarization System, depending on its topic-focused system learning ability.
- It can range from being a Standalone Topic-Focused Multi-Document Summarization System to being an Integrated Topic-Focused Multi-Document Summarization System, depending on its topic-focused system architecture.
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- It can implement Topic-Focused Multi-Document Summarization Algorithms for focused content extraction.
- It can participate in Document Understanding Conferences for system evaluation.
- It can support Complex Question Answering Tasks through topic-guided synthesis.
- It can integrate with Information Retrieval Systems for document selection.
- It can interface with Query Processing Systems for topic analysis.
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- Example(s):
- SQuASH System, implementing semantic role labeling for question-based summarization.
- DUC Participant Systems, such as:
- Research Systems, such as:
- Topic-MEAD System, using topic-weighted centroids.
- Query-Focused TextRank, applying biased graph ranking.
- NeATS System, for news topic summarization.
- Commercial Systems, such as:
- NewsBlaster System, for topic-focused news aggregation.
- Google News Summarization, organizing topic clusters.
- Domain-Specific Systems, such as:
- ...
- Counter-Example(s):
- Generic Multi-Document System, lacking topic focus.
- Single-Document System, processing only one document.
- Keyword-Based System, using keyword lists rather than topic specifications.
- Random Extraction System, without topic guidance.
- Full Translation System, preserving all content rather than topic-focused selection.
- See: Multi-Document Summarization System, Topic-Focused Multi-Document Summarization Algorithm, SQuASH System, Document Understanding Conference, Query-Focused Summarization, Information Retrieval System, Question Answering System.
References
2008
- (Wan, 2008) ⇒ Xiaojun Wan. (2008). "Using only cross-document relationships for both generic and topic-focused multi-document summarizations." In: Information Retrieval, 11(1).
2006
- (Dang, 2006) ⇒ Hoa Trang Dang. (2006). "Overview of DUC 2006." In: Proceedings of Document Understanding Conference.
2005
- (Dang, 2005) ⇒ Hoa Trang Dang. (2005). "Overview of DUC 2005." In: Proceedings of Document Understanding Conference.
2004
- (Erkan & Radev, 2004) ⇒ Günes Erkan and Dragomir R. Radev. (2004). "LexPageRank: Prestige in Multi-Document Text Summarization." In: Proceedings of EMNLP 2004.