Introduces the Matter to Mechanism benchmark of 2,645 structured instances and a composite metric suite for evaluating AI co-scientists on problem-to-hypothesis reasoning in battery materials research.
ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Evaluation of summarization tasks is extremely crucial to determining the quality of machine generated summaries. Over the last decade, ROUGE has become the standard automatic evaluation measure for evaluating summarization tasks. While ROUGE has been shown to be effective in capturing n-gram overlap between system and human composed summaries, there are several limitations with the existing ROUGE measures in terms of capturing synonymous concepts and coverage of topics. Thus, often times ROUGE scores do not reflect the true quality of summaries and prevents multi-faceted evaluation of summaries (i.e. by topics, by overall content coverage and etc). In this paper, we introduce ROUGE 2.0, which has several updated measures of ROUGE: ROUGE-N+Synonyms, ROUGE-Topic, ROUGE-Topic+Synonyms, ROUGE-TopicUniq and ROUGE-TopicUniq+Synonyms; all of which are improvements over the core ROUGE measures.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.
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Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes
Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.