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MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization ...
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Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline ...
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Fine-grained Factual Consistency Assessment for Abstractive Summarization Models ...
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Modeling Endorsement for Multi-Document Abstractive Summarization ...
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COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images ...
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Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets ...
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A Large-Scale Dataset for Empathetic Response Generation ...
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Knowledge and Keywords Augmented Abstractive Sentence Summarization ...
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Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior ...
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Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation ...
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Sentence-level Planning for Especially Abstractive Summarization ...
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Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences ...
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Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation ...
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Measuring Similarity of Opinion-bearing Sentences ...
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Abstract:
For many NLP applications of online reviews, comparing two opinion-bearing sentences is the key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts. We address this gap by studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of “opinion similarity” but not “opinion difference". Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence embedding network based on ...
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Keyword:
Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
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URL: https://dx.doi.org/10.48448/0nt8-1w36 https://underline.io/lecture/39821-measuring-similarity-of-opinion-bearing-sentences
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Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ...
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