Semantic Subgraph-Based Sentence Selection Method
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A Semantic Subgraph-Based Sentence Selection Method is a graph-based sentence selection method that extracts relevant sentences by analyzing semantic subgraphs within document semantic graphs.
- AKA: Semantic Graph-Based Sentence Selection, Subgraph-Based Sentence Extraction Method, Semantic Subgraph Sentence Selection.
- Context:
- It can typically construct Semantic Subgraph-Based Document Graphs from semantic subgraph-based source documents.
- It can typically identify Semantic Subgraph-Based Relevant Nodes using semantic subgraph-based node importance metrics.
- It can typically compute Semantic Subgraph-Based Edge Weights based on semantic subgraph-based relation strength.
- It can typically extract Semantic Subgraph-Based Sentence Clusters through semantic subgraph-based graph partitioning.
- It can typically rank Semantic Subgraph-Based Sentences using semantic subgraph-based centrality measures.
- It can typically preserve Semantic Subgraph-Based Coherence by maintaining semantic subgraph-based structural integrity.
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- It can often integrate with Semantic Role Labeling Systems for semantic subgraph-based semantic annotation.
- It can often utilize Semantic Subgraph-Based Learning Algorithms for semantic subgraph-based pattern recognition.
- It can often apply Semantic Subgraph-Based Optimization for semantic subgraph-based computational efficiency.
- It can often incorporate Semantic Subgraph-Based Features including semantic subgraph-based connectivity and semantic subgraph-based density.
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- It can range from being a Simple Semantic Subgraph-Based Sentence Selection Method to being a Complex Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based selection complexity.
- It can range from being a Single-Graph Semantic Subgraph-Based Sentence Selection Method to being a Multi-Graph Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based graph scope.
- It can range from being an Unsupervised Semantic Subgraph-Based Sentence Selection Method to being a Supervised Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based learning requirement.
- It can range from being a Static Semantic Subgraph-Based Sentence Selection Method to being a Dynamic Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based adaptation capability.
- It can range from being a Domain-General Semantic Subgraph-Based Sentence Selection Method to being a Domain-Specific Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based domain specialization.
- It can range from being an Exact Semantic Subgraph-Based Sentence Selection Method to being an Approximate Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based selection precision.
- It can range from being a Shallow Semantic Subgraph-Based Sentence Selection Method to being a Deep Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based analysis depth.
- It can range from being a Fast Semantic Subgraph-Based Sentence Selection Method to being a Comprehensive Semantic Subgraph-Based Sentence Selection Method, depending on its semantic subgraph-based processing thoroughness.
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- It can be implemented in SQuASH Algorithm for question-based summarization.
- It can be evaluated using Graph-Based Selection Metrics including subgraph coverage and selection precision.
- It can integrate with Document Summarization Systems for content extraction.
- It can interface with Semantic Processing Pipelines for document analysis.
- It can support Multi-Document Summarization Tasks through cross-document subgraph analysis.
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- Example(s):
- SQuASH Semantic Subgraph Selection, implemented in DUC-2005 and DUC-2006 systems.
- Leskovec et al. (2004) Subgraph Method, learning document semantic sub-structures.
- Graph-Based Implementations, such as:
- Domain-Specific Implementations, such as:
- Research System Implementations, such as:
- ...
- Counter-Example(s):
- Frequency-Based Sentence Selection, using word frequency without semantic graph analysis.
- Position-Based Sentence Selection, relying on sentence location rather than semantic structure.
- Random Sentence Selection, lacking semantic analysis.
- Keyword-Based Sentence Selection, matching terms without graph representation.
- Syntactic Tree Selection, using grammatical structure rather than semantic graphs.
- See: Graph-Based Sentence Selection Method, Document Semantic Graph, Semantic Role Labeling, SQuASH Algorithm, Graph-Based NLP Algorithm, Subgraph Mining Algorithm, Sentence Extraction Method.
References
2007
- (Shi et al., 2007) ⇒ Zhongmin Shi, Gabor Melli, Yang Wang, Yudong Liu, Baohua Gu, Mehdi M. Kashani, Anoop Sarkar, and Fred Popowich. (2007). "Question Answering Summarization of Multiple Biomedical Documents." In: Advances in Artificial Intelligence, Canadian AI 2007.
2005
- (Melli et al., 2005) ⇒ Gabor Melli, Yang Wang, Yudong Liu, Mehdi M. Kashani, Zhongmin Shi, Baohua Gu, Anoop Sarkar, and Fred Popowich. (2005). "Description of SQUASH, the SFU Question Answering Summary Handler for the DUC-2005 Summarization Task." In: Proceedings of DUC 2005.
2004
- (Leskovec et al., 2004) ⇒ Jure Leskovec, Marko Grobelnik, and Natasa Milic-Frayling. (2004). "Learning Sub-structures of Document Semantic Graphs for Document Summarization." In: Proceedings of LinkKDD 2004.
2004
- (Mihalcea & Tarau, 2004) ⇒ Rada Mihalcea and Paul Tarau. (2004). "TextRank: Bringing Order into Texts." In: Proceedings of EMNLP 2004.