Question-Based Multi-Document Text Summarization Task
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A Question-Based Multi-Document Text Summarization Task is a topic-focused multi-document text summarization task that generates question-focused summaries from multiple documents in response to complex questions.
- AKA: Query-Based Multi-Document Summarization Task, Question-Focused Multi-Document Summarization Task, Question-Driven Multi-Document Summarization Task.
- Context:
- It can typically process Question-Based Document Collections containing 25-50 question-based source documents.
- It can typically analyze Question-Based Complex Questions requiring question-based multi-faceted answers.
- It can typically produce Question-Based 250-Word Summaries per DUC specifications.
- It can typically employ Question-Based Semantic Analysis including question-based semantic role labeling.
- It can typically maintain Question-Based Answer Coherence across question-based information sources.
- It can typically ensure Question-Based Factual Accuracy while synthesizing information.
- ...
- It can often incorporate Question-Based Relevance Scoring for question-based content selection.
- It can often apply Question-Based Redundancy Control across question-based document sources.
- It can often utilize Question-Based Background Knowledge for question-based context understanding.
- It can often generate Question-Based Update Summaries for question-based temporal documents.
- ...
- It can range from being a Simple Question-Based Multi-Document Text Summarization Task to being a Complex Question-Based Multi-Document Text Summarization Task, depending on its question-based summarization complexity.
- It can range from being a Factoid Question-Based Multi-Document Text Summarization Task to being an Analytical Question-Based Multi-Document Text Summarization Task, depending on its question-based answer depth.
- It can range from being a Single-Aspect Question-Based Multi-Document Text Summarization Task to being a Multi-Aspect Question-Based Multi-Document Text Summarization Task, depending on its question-based facet coverage.
- It can range from being a Closed-Domain Question-Based Multi-Document Text Summarization Task to being an Open-Domain Question-Based Multi-Document Text Summarization Task, depending on its question-based domain scope.
- It can range from being an Extractive Question-Based Multi-Document Text Summarization Task to being an Abstractive Question-Based Multi-Document Text Summarization Task, depending on its question-based generation method.
- It can range from being a Generic Question-Based Multi-Document Text Summarization Task to being a Personalized Question-Based Multi-Document Text Summarization Task, depending on its question-based user adaptation.
- It can range from being a Monolingual Question-Based Multi-Document Text Summarization Task to being a Cross-Lingual Question-Based Multi-Document Text Summarization Task, depending on its question-based language processing.
- It can range from being a Short-Answer Question-Based Multi-Document Text Summarization Task to being a Long-Answer Question-Based Multi-Document Text Summarization Task, depending on its question-based response length.
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- It can be solved by Question-Based Multi-Document Summarization Systems implementing question-based summarization algorithms.
- It can be evaluated using DUC Evaluation Frameworks including linguistic quality questions.
- It can support Complex Question Answering beyond simple fact retrieval.
- It can integrate with Information Retrieval Systems for document selection.
- It can interface with Question Analysis Systems for query understanding.
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- Example(s):
- DUC Question-Based Tasks, such as:
- DUC-2005 Summarization Task, requiring question-based synthesis from 25-50 documents.
- DUC-2006 Summarization Task, generating fluent answers to complex questions from 25 documents.
- DUC-2007 Update Task, producing question-based update summaries.
- TAC Question-Based Tasks, such as:
- System Implementations, such as:
- Domain-Specific Tasks, such as:
- Biomedical Question Summarization, answering clinical questions from medical literature.
- Legal Question Summarization, addressing legal queries from case documents.
- Scientific Question Summarization, synthesizing research answers from academic papers.
- Benchmark Datasets, such as:
- MS MARCO Question Task, processing web documents for question answers.
- WikiQA Summarization Task, using Wikipedia articles for question responses.
- ...
- DUC Question-Based Tasks, such as:
- Counter-Example(s):
- Generic Multi-Document Summarization Task, lacking question focus.
- Single-Document Summarization Task, processing only one document.
- Topic-Based Summarization Task, using topic statements rather than questions.
- Keyword-Based Summarization Task, guided by keyword lists rather than natural language questions.
- Aspect-Based Summarization Task, following predefined aspects rather than question guidance.
- See: Topic-Focused Multi-Document Text Summarization Task, Multi-Document Text Summarization Task, Complex Question Answering Task, DUC Summarization Task, TAC Summarization Task, SQuASH Algorithm, Query-Focused Summarization.
References
2006
- (Dang, 2006) ⇒ Hoa Trang Dang. (2006). "Overview of DUC 2006." In: Proceedings of Document Understanding Conference (DUC 2006).
- QUOTE: The DUC 2006 summarization task was to synthesize from a set of 25 documents a well-organized, fluent answer to a complex question.
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.
2005
- (Dang, 2005) ⇒ Hoa Trang Dang. (2005). "Overview of DUC 2005." In: Proceedings of Document Understanding Conference (DUC 2005).
- QUOTE: The summarization task was to synthesize from a set of 25-50 documents a well-organized, fluent answer to a complex question.
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.