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Found 7 papers, 1 papers with code
Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE?
One fundamental contribution of the paper is that it demonstrates how we can generate more reliable semantic-aware ground truths for evaluating extractive summarization tasks without any additional human intervention.
Semantic Overlap Summarization among Multiple Alternative Narratives: An Exploratory Study
In this paper, we introduce an important yet relatively unexplored NLP task called Semantic Overlap Summarization (SOS), which entails generating a single summary from multiple alternative narratives which can convey the common information provided by those narratives.
Benchmarking LLMs on the Semantic Overlap Summarization Task
While recent advancements in Large Language Models (LLMs) have achieved superior performance in numerous summarization tasks, a benchmarking study of the SOS task using LLMs is yet to be performed.
Prompting LLMs to Compose Meta-Review Drafts from Peer-Review Narratives of Scholarly Manuscripts
One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves understanding the core contributions, strengths, and weaknesses of a scholarly manuscript based on peer-review narratives from multiple experts and then summarizing those multiple experts' perspectives into a concise holistic overview.
The Daunting Dilemma with Sentence Encoders: Success on Standard Benchmarks, Failure in Capturing Basic Semantic Properties
In this paper, we adopted a retrospective approach to examine and compare five existing popular sentence encoders, i. e., Sentence-BERT, Universal Sentence Encoder (USE), LASER, InferSent, and Doc2vec, in terms of their performance on downstream tasks versus their capability to capture basic semantic properties.
Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark
In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives.
SAM: The Sensitivity of Attribution Methods to Hyperparameters
Attribution methods can provide powerful insights into the reasons for a classifier's decision.
