SQuASH Algorithm
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A SQuASH Algorithm is a topic-focused multi-document summarization algorithm that integrates semantic role labeling with semantic subgraph-based sentence selection for question-based summarization.
- AKA: SFU Question Answering Summary Handler Algorithm, SQUASH Summarization Algorithm.
- Context:
- It can typically process SQuASH Algorithm Document Collections of 25-50 SQuASH algorithm source documents per DUC specifications.
- It can typically analyze SQuASH Algorithm Complex Questions to guide SQuASH algorithm content extraction.
- It can typically employ SQuASH Algorithm Semantic Role Labeling using PropBank annotations and SVM classifiers.
- It can typically perform SQuASH Algorithm Semantic Subgraph Selection from SQuASH algorithm document semantic graphs.
- It can typically apply SQuASH Algorithm Sentence Compression using statistical compression methods.
- It can typically execute SQuASH Algorithm Automatic Post-Editing for SQuASH algorithm coherence improvement.
- It can typically generate SQuASH Algorithm 250-Word Summaries as SQuASH algorithm final output.
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- It can often utilize SQuASH Algorithm Linguistic Resources including WordNet lexical database and PropBank corpus.
- It can often incorporate SQuASH Algorithm Cohesion Analysis based on Halliday-Hasan cohesion framework.
- It can often apply SQuASH Algorithm Multi-Document Fusion for cross-document information integration.
- It can often optimize SQuASH Algorithm Performance through parameter tuning and feature selection.
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- It can range from being a DUC-2005 SQuASH Algorithm to being a DUC-2006 SQuASH Algorithm, depending on its SQuASH algorithm version evolution.
- It can range from being a Baseline SQuASH Algorithm to being an Enhanced SQuASH Algorithm, depending on its SQuASH algorithm component integration.
- It can range from being a Research SQuASH Algorithm to being a Competition SQuASH Algorithm, depending on its SQuASH algorithm deployment context.
- It can range from being a Single-Stage SQuASH Algorithm to being a Multi-Stage SQuASH Algorithm, depending on its SQuASH algorithm processing pipeline.
- It can range from being a Domain-General SQuASH Algorithm to being a Domain-Adapted SQuASH Algorithm, depending on its SQuASH algorithm specialization.
- It can range from being an Extractive SQuASH Algorithm to being a Hybrid SQuASH Algorithm, depending on its SQuASH algorithm generation method.
- It can range from being a Rule-Based SQuASH Algorithm to being a Learning-Based SQuASH Algorithm, depending on its SQuASH algorithm adaptation approach.
- It can range from being a Fast SQuASH Algorithm to being a Comprehensive SQuASH Algorithm, depending on its SQuASH algorithm processing depth.
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- It can be implemented by SQuASH Systems for DUC competition participation.
- It can be evaluated using DUC Evaluation Metrics including ROUGE metrics and pyramid scores.
- It can pioneer Semantic Role Labeling Application in question answering systems.
- It can influence Subsequent Summarization Algorithms through algorithm innovations.
- It can contribute to SQuASH Research Project objectives at Simon Fraser University.
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- Example(s):
- DUC-2005 SQuASH Algorithm, the initial implementation (Melli et al., 2005).
- DUC-2006 SQuASH Algorithm, the enhanced version (Melli et al., 2006).
- SQuASH Algorithm Modules, such as:
- SQuASH SRL Module, implementing semantic role labeling with SVMs.
- SQuASH Subgraph Module, performing semantic subgraph extraction.
- SQuASH Compression Module, applying sentence compression (Knight & Marcu, 2000).
- SQuASH Post-Edit Module, executing automatic refinement.
- SQuASH Algorithm Processing Stages, such as:
- SQuASH Preprocessing Stage, performing document analysis and question parsing.
- SQuASH Extraction Stage, selecting relevant sentences.
- SQuASH Postprocessing Stage, refining summary output.
- SQuASH Algorithm Variants, such as:
- ...
- Counter-Example(s):
- Generic Summarization Algorithm, lacking question focus and semantic role analysis.
- Frequency-Based Algorithm, using term frequency without semantic processing.
- Single-Document Algorithm, processing only one document.
- Pure Abstractive Algorithm, generating only new text without extraction.
- Position-Based Algorithm, relying on sentence position rather than semantic relevance.
- See: Topic-Focused Multi-Document Summarization Algorithm, Semantic Role Labeling, Semantic Subgraph-Based Sentence Selection, SQuASH System, SQuASH Research Project, DUC-2005 Summarization Task, DUC-2006 Summarization Task, Question-Based Multi-Document Summarization Algorithm.
References
2006
- (Melli et al., 2006) ⇒ Gabor Melli, Zhongmin Shi, Yang Wang, Yudong Liu, Anoop Sarkar, and Fred Popowich. (2006). "Description of SQUASH, the SFU Question Answering Summary Handler for the DUC-2006 Summarization Task." In: Proceedings of DUC 2006.
- QUOTE: This paper describes the design of the SQUASH system... The SQUASH system for DUC-2006 built upon the SQUASH system that was built for last year's DUC-2005 competition.
2005
- (Melli et al., 2005) ⇒ Gabor Melli, Yang Wang, Yudong Liu, M. 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 the Document Understanding Conference (DUC-2005).