| Challenge: | Practical summarization systems are expected to produce summaries of varying lengths, per user needs. |
| Approach: | They propose to use ROUGE metric to evaluate system summaries of multiple lengths. |
| Outcome: | The evaluation protocol in question is competitive, the authors show . they found that the evaluation protocol is competitive with existing benchmarks. |
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How well do you know your summarization datasets? (2021.findings-acl)
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| Challenge: | State-of-the-art summarization systems are trained on massive datasets scraped from the web. |
| Approach: | They manually analyse 600 samples from three popular summarization datasets . they use a six-class typology which captures different noise types and degrees of summarizing difficulty. |
| Outcome: | The proposed model performs better on large datasets than on the current models. |
How “Multi” is Multi-Document Summarization? (2022.emnlp-main)
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| Challenge: | Multi-document summarization (MDS) aims at combining information spread across multiple documents . a single document often covers the full summary content . |
| Approach: | They propose a measure to evaluate the degree to which a summary is "disperse" they propose to combine information from multiple documents into a single document to generate a concise summary . |
| Outcome: | The proposed measure evaluates the degree to which a summary is "disperse" the measure is applied to several popular MDS datasets and state-of-the-art systems. |
How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)
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| Challenge: | Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source. |
| Approach: | They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics. |
| Outcome: | The proposed model can generate more relevant summaries but not factual ones. |
Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics (2022.naacl-main)
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| Challenge: | Existing definitions of system-level correlations are inconsistent with how they are used to evaluate systems. |
| Approach: | They propose to calculate correlations only on pairs of systems separated by small differences in automatic scores . they propose to use the full test set instead of the subset of summaries judged by humans . |
| Outcome: | The proposed changes improve the accuracy of the estimated correlations on pairs of systems separated by small differences in automatic scores. |
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments . |
| Approach: | They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization . |
| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
Studying Summarization Evaluation Metrics in the Appropriate Scoring Range (P19-1)
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| Challenge: | Existing evaluation metrics are compared based on their ability to correlate with humans, but they disagree in the higher-scoring range in which current systems operate. |
| Approach: | They show that evaluation metrics which behave similarly on these datasets strongly disagree in the higher-scoring range in which current systems operate. |
| Outcome: | The evaluation metrics which behave similarly on these datasets strongly disagree in the higher-scoring range in which current systems operate. |
How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation (2022.coling-1)
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| Challenge: | Existing methods to evaluate summary coherence are often evaluated using disparate datasets and metrics. |
| Approach: | They propose to use automatic evaluation to evaluate coherence of summaries by selecting high-scoring candidates. |
| Outcome: | The proposed methods show that they can perform better on an even playing field. |
Length Does Matter: Summary Length can Bias Summarization Metrics (2023.emnlp-main)
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| Challenge: | Existing summarization metrics favor shorter or longer summaries, but evaluations of these metrics are flawed. |
| Approach: | They propose a Bayesian normalization technique that effectively diminishes this bias. |
| Outcome: | The proposed method significantly improves the concordance between human annotators and most metrics in terms of summary coherence. |
Proceedings of the 2nd Workshop on New Frontiers in Summarization (D19-54)
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| Challenge: | EMNLP 2017 is a workshop on enhancing natural language processing's ability to produce concise, fluent summaries. |
| Approach: | the workshop provides a forum for cross-fertilization of ideas towards automatic summarization . four invited speakers will be present at the workshop . |
| Outcome: | the workshop aims to provide a forum for cross-fertilization of ideas towards automatic summarization. |
DialSummEval: Revisiting Summarization Evaluation for Dialogues (2022.naacl-main)
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| Challenge: | Current models for dialogue summarization have flaws that may not be well exposed by frequently used metrics such as ROUGE. |
| Approach: | They propose to re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models for the first time. |
| Outcome: | The proposed dataset will be used to evaluate 18 categories of metrics in terms of coherence, consistency, fluency and relevance, and a unified human evaluation of various models for the first time. |