Entire-Document Summarization Task

An Entire-Document Summarization Task is a document summarization task that entails generating a concise, informative summary that encapsulates all critical content and themes of a complete document, irrespective of its length.

• Context:
  • It can (typically) be applied to documents of any length, from short articles to extensive reports, where the goal is to preserve essential information and insights without omitting significant content due to length constraints.
  • It can (often) involve the use of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to analyze and extract the main points from the entire document efficiently.
  • It can (typically) require strategies that ensure the retention of critical information and thematic consistency across different sections of the document, such as Global Contextual Understanding and Thematic Analysis.
  • It can range from being a Single-Document Text Summarization Task to being a Multi-Document Text Summarization Task.
  • It can range from being a Short-Document Summarization Task to being a Long-Document Summarization Task.
  • ...
• Example(s):
  • Creating an executive summary for a comprehensive annual report.
  • Summarizing a lengthy academic paper into an abstract that covers all its key contributions and findings.
  • Condensing a full-length novel into a synopsis that captures the plot, character development, and themes.
• Counter-Example(s):
  • Document-Section Summarization Task.
  • Contract-Redlining Summarization Task.
  • Generating keyword-based or topic-specific summaries that do not aim to cover the entire document.
  • Extractive summarization tasks that only pull out specific sentences or paragraphs without synthesizing a comprehensive summary.
• See:

Global Contextual Understanding, Thematic Analysis, Text Summarization Task, Natural Language Processing (NLP), Large Language Models (LLMs), Extractive Summarization Task, Abstractive Summarization Task.

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