Summary Generation Task
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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.
- ...
- a Text-Item Summarization Task to produce a text-item 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
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
- Summarize(input) => output
2023
- Template:Lb The act of summarising
- Jim is very adept at summarisation.
- Template:Lb The process of summarising
- After summarisation, these documents seem much simpler.
- Template:Lb A summary; the result of summarising
- Jim's summarisation of the incident was very useful.