| Challenge: | Summarization studies work on increasing the scores that are given by automatic evaluation measures. |
| Approach: | They propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements. |
| Outcome: | The proposed measure outperforms ROUGE and BE in most cases and achieves highest correlation coefficient in TAC 2011 AESOP task. |
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| Challenge: | Existing reference-based evaluation metrics such as ROUGE have their own drawbacks. |
| Approach: | They propose a protocol for a reference-based automatic evaluation metric that requires the endorsement of source document. |
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Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries (D18-1)
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| Challenge: | Existing methods for evaluating content are not accurate because they only confirm if the summary contains small textual fragments. |
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| Challenge: | Existing studies on automatic summary evaluation metrics focus on lexical similarity and require a reference summary which is expensive to obtain. |
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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. |
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| Challenge: | Experimental results show that automatic summarization generates concise summaries that contain key ideas of source documents. |
| Approach: | They propose to use Element-aware test sets to annotate news-related reference summaries to focus on more fine-grained news elements objectively and comprehensively. |
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| Challenge: | Existing methods to evaluate text summarization tasks using ROUGE have been criticized for lack of semantic understanding. |
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PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference (2026.acl-long)
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| Challenge: | Existing pruning methods ignore prefill-decode (PD) disaggregation in practice. |
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Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
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PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation (2024.findings-eacl)
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| Challenge: | Several approaches to parameter-efficient fine-tuning have been proposed . low-rank Adaptation (LoRA) does not consider the varying importance of each layer . |
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EASSE: Easier Automatic Sentence Simplification Evaluation (D19-3)
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| Challenge: | EASSE provides access to a broad range of evaluation resources including standard automatic metrics, word-level accuracy scores and reference-independent quality estimation features. |
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