Text Summarization Performance Measure

A Text Summarization Performance Measure is a text processing evaluation measure for text summarization items.

• AKA: Text Summary Evaluation Measure.
• Context:
  • input: a Summary Item.
  • output: a Text Summary Evaluation Score.
  • It can (typically) be an NLP Performance Measiure for a Text Summarization System's ability to solve a text summarization task.
  • It can (typically) involve evaluating the completeness and relevance of the content in the summary.
  • It can (often) include assessing whether key points from the original document are accurately captured and clearly presented.
  • It can involve assessing the Summary Coherence, Summary Relevance, and Summary Fluency.
  • It can inform a Summarization Application's usability.
  • It can involve checking the summary against a predefined template for formatting and content adherence.
  • It can be part of a broader quality assurance process to ensure summaries meet specific standards.
  • It can contribute to ensuring that summaries are concise, clear, and informative.
  • It can involve assessing the Summary Coherence, Summary Relevance, and Summary Fluency.
  • It can inform a Summarization Application's usability.
  • It can help identify errors, omissions, and areas for improvement in the summary.
  • It can include metrics like readability scores, coverage ratios, and precision-recall metrics.
  • ...
• Example(s):
  • Task-Specific Text Summarization Performance Measures, such as:
    • A Contract Summary Evaluation Measure, such as a redlined contract summary evaluation measure.
    • Medical Record Summary Evaluation Measure: This measure would assess how well a summarization system captures key patient information, diagnoses, treatments, and outcomes from lengthy medical records. It might focus on the accuracy of medical terminology, preservation of critical health data, and adherence to healthcare privacy standards.
    • Legal Case Brief Evaluation Measure: This measure would evaluate summaries of legal cases, focusing on accurately representing key legal principles, arguments, precedents, and rulings. It might assess how well the summary captures the legal reasoning and maintains the formal language typical of legal documents.
    • Scientific Paper Abstract Evaluation Measure: This measure would assess summaries or abstracts of scientific papers, focusing on how well they capture the research question, methodology, key findings, and implications. It might consider factors like technical accuracy, inclusion of essential statistical data, and adherence to the standard structure of scientific abstracts.
    • News Article Summarization Evaluation Measure: This measure would evaluate summaries of news articles, assessing factors like preservation of key facts, maintaining neutrality, capturing the 5W1H (Who, What, When, Where, Why, How), and reflecting the original article's tone and importance hierarchy.
    • Financial Report Summary Evaluation Measure: This measure would assess summaries of financial reports or earnings calls, focusing on accurately representing key financial metrics, market trends, risk factors, and forward-looking statements. It might consider compliance with financial reporting standards and the ability to distill complex financial data into clear, actionable insights.
    • ...
  • General-Purpose Text Summarization Performance Measures, such as:
    • ROUGE Metrics, which evaluates the quality of a summary by comparing it to reference summaries.
    • BLEU, typically used for machine translation but also applicable in summarization to assess the precision of generated summaries.
    • METEOR, which considers word-to-word matches between the generated summary and reference texts.
    • BERTScore, leveraging BERT embeddings to evaluate semantic similarity between generated and reference summaries.
    • Text Summarization Precision measures the proportion of content in the generated summary that is relevant or important.
    • Text Summarization Recall assesses how much of the important content from the original text is captured in the summary.
    • Reference and Document Aware Semantic Score (RDASS).
    • Summary Readability Measures.
  • ...
• Counter-Example(s):
  • A Novel Text Evaluation Measure, such as an essay evaluation measure.
  • A Machine Translation Performance Metric.
  • A Question-Answering Performance Measure.
• See: Automated Text Summarization, Content Coverage Ratio, Manual Text Summarization, Text Summarization Faithfulness Evaluation.

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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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