Summarization Post-Editing Task

A Summarization Post-Editing Task is a text post-editing task that refines generated summaries to improve summary quality.

• AKA: Summary Post-Editing Task, Summary Refinement Task, Summary Post-Processing Task.
• Context:
  • It can typically correct Summarization Post-Editing Errors including summarization grammatical errors and summarization coherence issues.
  • It can typically improve Summarization Post-Editing Quality through summarization fluency enhancement and summarization readability improvement.
  • It can typically ensure Summarization Post-Editing Consistency across summarization sentence boundaries and summarization paragraph transitions.
  • It can typically remove Summarization Post-Editing Redundancy from summarization repeated content and summarization overlapping information.
  • It can typically maintain Summarization Post-Editing Factual Accuracy while performing summarization content revision.
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  • It can often follow Sentence Selection in summarization post-editing pipelines.
  • It can often apply Summarization Post-Editing Rules for summarization standardization and summarization formatting.
  • It can often utilize Summarization Post-Editing Models trained on summarization post-editing corpora.
  • It can often integrate Summarization Post-Editing Feedback for summarization quality control.
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  • It can range from being a Rule-Based Summarization Post-Editing Task to being a Learning-Based Summarization Post-Editing Task, depending on its summarization post-editing mechanism.
  • It can range from being a Light Summarization Post-Editing Task to being a Heavy Summarization Post-Editing Task, depending on its summarization post-editing revision extent.
  • It can range from being a Manual Summarization Post-Editing Task to being an Automatic Summarization Post-Editing Task, depending on its summarization post-editing automation level.
  • It can range from being a Surface-Level Summarization Post-Editing Task to being a Deep Summarization Post-Editing Task, depending on its summarization post-editing semantic depth.
  • It can range from being a Single-Pass Summarization Post-Editing Task to being an Iterative Summarization Post-Editing Task, depending on its summarization post-editing iteration count.
  • It can range from being a Domain-General Summarization Post-Editing Task to being a Domain-Specific Summarization Post-Editing Task, depending on its summarization post-editing domain specialization.
  • It can range from being a Monolingual Summarization Post-Editing Task to being a Cross-Lingual Summarization Post-Editing Task, depending on its summarization post-editing language handling.
  • It can range from being a Real-Time Summarization Post-Editing Task to being a Batch Summarization Post-Editing Task, depending on its summarization post-editing processing mode.
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  • It can be performed by Summarization Post-Editing Systems implementing summarization post-editing algorithms.
  • It can be evaluated using Summarization Post-Editing Metrics including summarization post-editing improvement rate.
  • It can integrate with Summarization Pipelines for end-to-end summary generation.
  • It can interface with Summarization Quality Assurance Systems for summarization validation.
  • It can utilize Summarization Post-Editing Guidelines for summarization standard compliance.
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• Example(s):
  • SQuASH Automatic Post-Editing, refining question-based summaries.
  • DUC System Post-Editings, such as:
    • DUC-2005 Post-Editing, improving multi-document summaries.
    • DUC-2006 Post-Editing, enhancing complex question summaries.
  • Neural Post-Editing Systems, such as:
    • BERT-based Summary Post-Editing, using transformer models.
    • GPT-based Summary Post-Editing, applying language models.
  • Pipeline Post-Editing Stages, such as:
    • Extractive Summarization Post-Editing, after sentence extraction.
    • Abstractive Summarization Post-Editing, following text generation.
  • Domain-Specific Post-Editings, such as:
    • Medical Summary Post-Editing, for clinical summaries.
    • Legal Summary Post-Editing, for legal briefs.
    • News Summary Post-Editing, for news digests.
  • Quality-Focused Post-Editings, such as:
    • Coherence Post-Editing, improving summary flow.
    • Factuality Post-Editing, correcting factual errors.
    • Conciseness Post-Editing, reducing summary length.
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• Counter-Example(s):
  • Manual Post-Editing, requiring human intervention.
  • Pre-Editing Task, occurring before summary generation.
  • Machine Translation Post-Editing, refining translations rather than summaries.
  • Text Generation Task, creating new content rather than refining existing.
  • Text Validation Task, checking errors without correction.
• See: Text Editing Task, Summarization System, Post-Processing Task, Quality Assurance Task, SQuASH Algorithm, Automatic Post-Editing, Text Refinement Task.

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