Summary Generation Task

A Summary Generation Task is a linguistic generation task that requires generating summary items for one or more information resources.

• Context:
  • input: one or more Information Resources.
  • ouptut: a Summary Artifact.
  • performance measure: Summarization Performance Measure.
  • It can (typically) require critical thinking and analytical skills to identify the main ideas and relevant details from the source text.
  • It can range from being a Brief Summary Generation Task of a few sentences to a more detailed Executive Summary Writing Task spanning several pages.
  • It can range from being a Human Summary Generation Task to being an Automated Summary Generation Task.
  • It can range from being an Oral Summary Generation Task to being a Written Summary Generation Task.
  • It can range from being an Single-Item Summary Generation Task to being a Multi-Item Summary Generation Task.
  • It can range from being an Open-Topic Summary Generation Task to being a Domain-Specific Summary Generation Task.
  • It can be supported by a Summary Generation System (which can be evaluated by summarizer evaluation).
  • It can be instantiated in a Summarization Act.
  • ...
• Example(s):
  • a Text-Item Summarization Task to produce a text-item summary.
    • a Domain-Specific Summary Writing Task e.g. to produce a redlined contract summary.
  • a Rich-Text Document Summarization Task to produce a rich text document summary.
  • an Academic Paper Abstract Creation that condenses a research paper into an academic paper abstract.
  • a Business Summary Writing to produce a business summary.
  • a Summary-based Question-Answering Task to produce a Q&A summary-based answer.
  • an Image Summarization Task to produce an image summary.
  • a Graph Summarization Task to produce a graph summary.
  • an Ontology Summarization Task to produce an ontology summary.
  • a Source-Code Summarization Task to produce a source-code summary.
  • ...
• Counter-Example(s):
  • Full-Length Reports, which provide comprehensive details and in-depth analysis rather than a condensed version.
  • an Linguistic Translation Task.
  • a Paraphrasing Task.
• See: Abstract Writing Task, Executive Summary, Report Writing Task, Critical Analysis Task, Document Understanding

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References

2024

• Perplexity
  • A Summary Generation Task is a linguistic generation task that requires generating summary items for one or more information resources.
  • Evaluation:
    • **Human Evaluation**: Often considered the gold standard for evaluating summaries, involving criteria such as coherence, consistency, fluency, and relevance[1][2][3].
    • **Intrinsic vs. Extrinsic Evaluation**: Intrinsic evaluation focuses on the quality of the summary itself, while extrinsic evaluation assesses the usefulness of the summary in downstream tasks like question answering or text classification[6].
  • Human Evaluation Protocols:
    • **SummEval**: A comprehensive toolkit and dataset for evaluating summarization models, including human annotations along dimensions like coherence, consistency, fluency, and relevance[1].
    • **Atomic Content Units (ACUs)**: A protocol for evaluating the salience of summaries by dissecting them into fine-grained content units, aiming for high inter-annotator agreement[2].
    • **Reliability Studies**: Investigations into the reliability of human evaluations, highlighting the need for standardized procedures and best practices[3].
  • Importance of Human Summarization:
    • **Professional Writing**: Summarizing is a crucial skill in professional writing, ensuring that key points are conveyed succinctly and effectively[19].
    • **Communication Skills**: Effective summarization enhances understanding, clarity, and impactful message delivery in various communication contexts[15].
  • Citations:

[1] https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00373/100686/SummEval-Re-evaluating-Summarization-Evaluation
[2] https://aclanthology.org/2023.acl-long.228.pdf
[3] https://aclanthology.org/2021.humeval-1.10.pdf
[4] https://hyperskill.org/learn/step/25430
[5] https://eugeneyan.com/writing/abstractive/
[6] https://arxiv.org/abs/2305.15044
[7] https://openai.com/index/learning-to-summarize-with-human-feedback/
[8] https://paperswithcode.com/task/text-summarization
[9] http://www.gabormelli.com/RKB/Summarization_Task
[10] https://cookbook.openai.com/examples/evaluation/how_to_eval_abstractive_summarization
[11] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635391/
[12] https://aclanthology.org/2023.emnlp-main.158.pdf
[13] https://www.vskills.in/certification/tutorial/autosummarization-and-manual-summarization-classless-and-classful-routing-protocols-and-convergence/

2023

• Claude
  • Summarize(input) => output
    • Input can be multi-modal information - text, images, datasets, code, etc.
    • Output is a synthesized text summary
    • Length of output < length of original textual input (if input contains text)
    • Output highlights the most relevant "key points" from input
    • Output provides a high-level overview of the core "essence" or "gist" of input
    • Output conveys the central ideas, concepts, findings, conclusions, or topics contained in the input
    • Output serves as a "briefing" for consumers to quickly familiarize themselves with the input
    • Output identifies important details like: main entities, definitions, facts, conclusions, relationships, methodology, or takeaways from input
    • Output has cohesive, logical flow with clear, concise language constructs

2023

• https://en.wiktionary.org/wiki/summarisation#Noun

1. Template:Lb The act of summarising

   Jim is very adept at summarisation.

2. Template:Lb The process of summarising

   After summarisation, these documents seem much simpler.

3. Template:Lb A summary; the result of summarising

   Jim's summarisation of the incident was very useful.

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