Question-Based Text Summarization Task

A Question-Based Text Summarization Task is a topic-focused text summarization task that generates question-focused summaries guided by specific questions.

• AKA: Query-Based Text Summarization Task, Question-Focused Text Summarization Task, Question-Driven Summarization Task.
• Context:
  • It can typically process Question-Based Input Documents to extract question-based relevant information.
  • It can typically analyze Question-Based Query Specifications to understand question-based information needs.
  • It can typically generate Question-Based Summary Answers that directly address question-based query topics.
  • It can typically employ Question-Based Relevance Scoring to identify question-based salient content.
  • It can typically maintain Question-Based Answer Coherence while ensuring question-based completeness.
  • ...
  • It can often utilize Question-Based Semantic Analysis for question-based content understanding.
  • It can often apply Question-Based Extraction Strategies for question-based sentence selection.
  • It can often incorporate Question-Based Background Knowledge for question-based context enrichment.
  • It can often support Question-Based Information Synthesis across question-based source material.
  • ...
  • It can range from being a Single-Document Question-Based Text Summarization Task to being a Multi-Document Question-Based Text Summarization Task, depending on its question-based document scope.
  • It can range from being a Simple Question-Based Text Summarization Task to being a Complex Question-Based Text Summarization Task, depending on its question-based query complexity.
  • It can range from being a Factoid Question-Based Text Summarization Task to being an Analytical Question-Based Text Summarization Task, depending on its question-based answer type.
  • It can range from being an Extractive Question-Based Text Summarization Task to being an Abstractive Question-Based Text Summarization Task, depending on its question-based generation method.
  • It can range from being a Closed-Domain Question-Based Text Summarization Task to being an Open-Domain Question-Based Text Summarization Task, depending on its question-based domain coverage.
  • It can range from being a Static Question-Based Text Summarization Task to being an Interactive Question-Based Text Summarization Task, depending on its question-based user interaction.
  • It can range from being a Short-Answer Question-Based Text Summarization Task to being a Long-Answer Question-Based Text Summarization Task, depending on its question-based response length.
  • It can range from being a Monolingual Question-Based Text Summarization Task to being a Cross-Lingual Question-Based Text Summarization Task, depending on its question-based language handling.
  • ...
  • It can be solved by Question-Based Summarization Systems implementing question-based summarization algorithms.
  • It can be evaluated using Question-Based Evaluation Metrics including answer relevance and question coverage.
  • It can support Complex Question Answering beyond simple fact extraction.
  • It can integrate with Information Retrieval Systems for document selection.
  • It can interface with Question Analysis Systems for query understanding.
  • ...
• Example(s):
  • Single-Document Question-Based Tasks, such as:
    • Document QA Summarization Task, answering questions from single documents.
    • Article Question Summarization Task, generating question-based summaries from news articles.
    • Report Question Summarization Task, extracting answers from technical reports.
  • Multi-Document Question-Based Tasks, such as:
    • Question-Based Multi-Document Text Summarization Task, synthesizing from multiple sources.
    • Cross-Document Question Task, integrating answers across document collections.
  • DUC Question-Based Tasks, such as:
    • DUC 2005 Question Task, requiring complex question answers.
    • DUC 2006 Question Task, generating fluent responses.
    • DUC 2007 Update Question Task, producing temporal answers.
  • Domain-Specific Question Tasks, such as:
    • Medical Question Summarization Task, answering clinical questions.
    • Legal Question Summarization Task, addressing legal queries.
    • Scientific Question Summarization Task, responding to research questions.
  • System Implementation Tasks, such as:
    • SQuASH Question Task, using semantic role labeling.
    • BERT-QA Summarization Task, applying transformer models.
  • ...
• Counter-Example(s):
  • Generic Text Summarization Task, lacking question guidance.
  • Topic-Based Summarization Task, using topic descriptions 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 questions.
  • Headline Generation Task, creating titles rather than question answers.
• See: Topic-Focused Text Summarization Task, Question-Based Multi-Document Text Summarization Task, Question Answering Task, Query-Focused Summarization, Complex Question Answering, DUC Summarization Task, Information Need.

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References

2008

• (Daumé III & Marcu, 2008) ⇒ Hal Daumé III and Daniel Marcu. (2008). "A Noisy-Channel Model for Question-Based Summarization." In: Proceedings of ACL-08: HLT.

2006

• (Harabagiu et al., 2006) ⇒ Sanda Harabagiu, Finley Lacatusu, and Andrew Hickl. (2006). "Answering Complex Questions with Random Walk Models." In: Proceedings of SIGIR 2006.

2005

• (Dang, 2005) ⇒ Hoa Trang Dang. (2005). "Overview of DUC 2005." In: Proceedings of Document Understanding Conference.
  • QUOTE: The system task in 2005 will be to synthesize from a set of 25-50 documents a brief, well-organized, fluent answer to a need for information that cannot be met by just stating a name, date, quantity, etc.

2003

• (Lacatusu et al., 2003) ⇒ Finley Lacatusu, Andrew Hickl, Kirk Roberts, Ying Shi, Jeremy Bensley, Bryan Rink, Patrick Wang, and Lara Taylor. (2003). "LCC's GISTexter at DUC 2003: Description and Results." In: Proceedings of DUC 2003.

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