Text Summarization Performance Measure

A Text Summarization Performance Measure is a text processing evaluation measure that quantifies summarization system effectiveness for text summarization tasks.

• AKA: Text Summary Evaluation Measure, Summarization Quality Metric, Summary Assessment Measure, Text Summarization Evaluation Metric.
• Context:
  • It can typically evaluate Summary Content Coverage through summary completeness assessments.
  • It can typically measure Summary Relevance using summary importance scoring.
  • It can typically assess Summary Coherence via summary readability metrics.
  • It can typically quantify Summary Fluency through summary grammaticality evaluation.
  • It can typically determine Summary Factuality using summary accuracy checking.
  • It can often measure Summary Conciseness through summary compression ratios.
  • It can often evaluate Summary Informativeness using summary information density metrics.
  • It can often assess Summary Consistency via summary contradiction detection.
  • It can often quantify Summary Faithfulness through summary hallucination detection.
  • It can often determine Summary Bias using summary neutrality assessment.
  • It can range from being a Reference-Based Summary Performance Measure to being a Reference-Free Summary Performance Measure, depending on its summary evaluation approach.
  • It can range from being an Automatic Summary Performance Measure to being a Human Summary Performance Measure, depending on its summary evaluation method.
  • It can range from being a Lexical Summary Performance Measure to being a Semantic Summary Performance Measure, depending on its summary comparison level.
  • It can range from being a Single-Aspect Summary Performance Measure to being a Multi-Aspect Summary Performance Measure, depending on its summary evaluation scope.
  • It can range from being an Extractive Summary Performance Measure to being an Abstractive Summary Performance Measure, depending on its summary generation type.
  • It can integrate with Summary Benchmark Datasets for summary standardized evaluation.
  • It can support Summary System Comparisons through summary normalized scoring.
  • It can inform Summarization Application usability assessments.
  • ...
• Examples:
  • Reference-Based Summary Performance Measures, such as:
    • ROUGE Metric Family, such as:
      • ROUGE-N for summary n-gram overlap evaluation.
      • ROUGE-L for summary longest common subsequence evaluation.
      • ROUGE-W for summary weighted longest common subsequence evaluation.
      • ROUGE-S for summary skip-bigram evaluation.
    • BLEU Score for summary precision evaluation.
    • METEOR Score for summary word-to-word matching.
    • BERTScore for summary semantic similarity evaluation.
    • MoverScore for summary contextualized embedding evaluation.
  • Reference-Free Summary Performance Measures, such as:
    • SUSWIR (Summary Score Without Reference) for summary quality without references.
    • SummaQA for summary question-answering evaluation.
    • BLANC for summary cloze test evaluation.
    • Summary Readability Scores for summary comprehension assessment.
  • Task-Specific Summary Performance Measures, such as:
    • Contract Summary Evaluation Measures, such as:
      • Redlined Contract Summary Evaluation Measure for summary legal change tracking.
      • Contract Clause Coverage Measure for summary legal completeness.
    • Medical Summary Evaluation Measures, such as:
      • Clinical Note Summary Evaluation for summary medical accuracy.
      • Patient Record Summary Assessment for summary healthcare compliance.
    • Scientific Summary Evaluation Measures, such as:
      • Research Abstract Quality Measure for summary scientific rigor.
      • Technical Paper Summary Evaluation for summary methodology preservation.
    • News Summary Evaluation Measures, such as:
      • News Article Summary Assessment for summary factual accuracy.
      • Breaking News Summary Evaluation for summary timeliness assessment.
    • Financial Summary Evaluation Measures, such as:
      • Earnings Report Summary Measure for summary financial accuracy.
      • Market Analysis Summary Evaluation for summary trend preservation.
  • Semantic-Based Summary Performance Measures, such as:
    • RDASS (Reference and Document Aware Semantic Score) for summary comprehensive evaluation.
    • Pyramid Score for summary content unit evaluation.
    • PEAK Score for summary proposition evaluation.
  • Factuality-Based Summary Performance Measures, such as:
    • FactCC for summary factual consistency checking.
    • QAGS (Question Answering and Generation for Summarization) for summary factual verification.
    • DAE (Dependency Arc Entailment) for summary entailment evaluation.
  • Multi-Dimensional Summary Performance Measures, such as:
    • SummEval Framework for summary comprehensive assessment.
    • UniEval for summary unified evaluation.
    • G-Eval for summary GPT-based evaluation.
  • ...
• Counter-Examples:
  • Machine Translation Performance Measure, which evaluates translation quality rather than summary condensation quality.
  • Question-Answering Performance Measure, which assesses answer accuracy rather than summary completeness.
  • Text Classification Performance Measure, which measures classification accuracy rather than summary quality.
  • Information Extraction Performance Measure, which evaluates extraction precision rather than summary coherence.
  • Essay Evaluation Measure, which assesses original writing quality rather than summary condensation effectiveness.
• See: Text Summarization Task, Automated Text Summarization, Manual Text Summarization, Content Coverage Ratio, Text Summarization Faithfulness Evaluation, Summary Evaluation Task, Summary Item, Summarization System, ROUGE Metric, BERTScore, Summary Coherence, Summary Relevance, Summary Fluency.

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References

2024

• LLM
  • Imagine you're a researcher working on cutting-edge natural language processing technology, tasked with evaluating a new text summarization system's performance. This is where Text Summarization Performance Measures come into play, acting like a report card for your system. They assess how well your AI captures and condenses the essence of longer texts into concise, meaningful summaries, ensuring the preservation of the core message, coherence, and relevance.

    In the evaluation process, you use a variety of evaluation metrics. ROUGE compares AI-generated summaries to human-written ones by looking for overlapping phrases and key points. BERTScore leverages large language models to assess semantic similarity. Beyond numerical metrics, you consider readability, fluency, and coherence to capture the human element.

    Evaluation challenges arise when some metrics don't align with human judgment or fail to capture quality aspects apparent to human readers. This blend of art and science underscores your expertise as a researcher. Your goal is to achieve a holistic understanding of your system's performance, producing summaries that are accurate, relevant, and engaging for real-world users.

2023

• (Yun et al., 2023) ⇒ Jiseon Yun, Jae Eui Sohn, and Sunghyon Kyeong. (2023). “Fine-Tuning Pretrained Language Models to Enhance Dialogue Summarization in Customer Service Centers.” In: Proceedings of the Fourth ACM International Conference on AI in Finance. doi:10.1145/3604237.3626838
  • QUOTE: ... The results demonstrated that the fine-tuned model based on KakaoBank’s internal datasets outperformed the reference model, showing a 199% and 12% improvement in ROUGE-L and RDASS, respectively. ...
  • QUOTE: ... RDASS is a comprehensive evaluation metric that considers the relationships among the original document, reference summary, and model-generated summary. Compared to ROUGE, RDASS performed better in terms of relevance, consistency, and fluency of sentences in Korean. Therefore, we employed both ROUGE and RDASS as evaluation metrics, considering their respective strengths and weaknesses of each metric. ...
  • QUOTE: ... RDASS measures the similarity between the vectors of the original document and reference summary. Moreover, it measures the similarity between the vectors of the original document and generated summary. Finally, RDASS can be obtained by computing their average. ...

2023

• (Foysal & Böck, 2023) ⇒ Abdullah Al Foysal, and Ronald Böck. (2023). “Who Needs External References?—Text Summarization Evaluation Using Original Documents.” In: AI, 4(4). doi:10.3390/ai4040049
  • NOTEs:
    • It introduces a new metric, SUSWIR (Summary Score without Reference), which evaluates automatic text summarization quality by considering Semantic Similarity, Relevance, Redundancy, and Bias Avoidance, without requiring human-generated reference summaries.
    • It emphasizes the limitations of traditional text summarization evaluation methods like ROUGE, BLEU, and METEOR, particularly in situations where no reference summaries are available, motivating the need for a more flexible and unbiased approach.
    • It demonstrates SUSWIR's effectiveness through extensive testing on various datasets, including CNN/Daily Mail and BBC Articles, showing that this new metric provides reliable and consistent assessments compared to traditional methods.

2023

• (Liu et al., 2023) ⇒ Yu Lu Liuu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu, and Adam Trischler. (2023). “Responsible AI Considerations in Text Summarization Research: A Review of Current Practices.” arXiv preprint arXiv:2311.11103.
  • NOTE:
  • It emphasizes the growing need for reflection on Ethical Considerations, adverse impacts, and other Responsible AI (RAI) issues in AI and NLP Tasks, with a specific focus on Text Summarization.
  • It explores how bias and Ethical Considerations are addressed, providing context for their own investigation in Text Summarization.
  • It discusses the importance and challenges of Text Summarization as a crucial NLP Task and the associated risks, such as producing incorrect, biased, or harmful summaries.
  • It examines the types of work prioritized in the community, common Text Summarization Evaluation Practices, and how Ethical Issues and limitations of work are addressed.
  • It details the Text Summarization Evaluation Practices, such as ROUGE Metrics, and their limitations, including potential biases and discrepancies with Human Judgment.
  • It reviews existing work on RAI in automated text summarization, exploring issues like Fairness, representation of Demographic Groups, and biases in Language Varieties.
  • It draws on previous NLP Meta-Analysises.
  • It analyses 333 Summarization Research Papers from the ACL Anthology published between 2020 and 2022.
  • It includes an Annotation Scheme that covers aspects related to paper goals, authors, Text Summarization Evaluation Practices, Stakeholders, limitations, and Ethical Considerations, providing a structured framework for analysis.
  • It reveals key findings about the community's focus on developing new systems, discrepancies in Text Summarization Evaluation Practices, and a lack of engagement with Ethical Considerations and limitations in most papers.

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