| Challenge: | 'Spoilers' on review websites can be a concern for consumers who want to fully experience the excitement of media consumption. |
| Approach: | They propose to use a large-scale book review dataset to generate fine-grained spoiler annotations . they then use supervised neural networks to detect spoiler sentences in review corpora . |
| Outcome: | The proposed method outperforms baselines in a large-scale book review dataset . it can detect spoiler sentences in review corpora, but only a few users use it . |
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| Challenge: | Existing models focus on the textual content of the review, while spoiler detection requires putting the review into the context of facts and knowledge regarding movies. |
| Approach: | They propose a network-based spoiler detection model that takes into account external knowledge about movies and user activities on movie review platforms. |
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Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization (2023.findings-acl)
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| Challenge: | Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarizing systems. |
| Approach: | They propose a task of fine-grained inconsistency detection to detect inconsistent summaries . they represent facts with semantic frames extracted by semantic role labeling and highlight related semantic frames to predict inconsistance . |
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An Empirical Examination of Online Restaurant Reviews (2020.lrec-1)
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| Challenge: | Existing methods for opinion mining and sentiment analysis focus on extracting either positive or negative opinions from texts and determining the targets of these opinions. |
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Spoiler Detection as Semantic Text Matching (2023.emnlp-main)
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| Challenge: | Existing research on spoiler detection shows promising results in safeguarding viewers from general spoilers, but it fails to address the issue of users abstaining from show-related content during their watch. |
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“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism (2021.eacl-main)
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| Challenge: | Several attention-based spoiler detection models are insufficient for utilizing dependency relations between context words. |
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Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021.emnlp-main)
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Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt Gardner
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Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
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| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
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Corpus Considerations for Annotator Modeling and Scaling (2024.naacl-long)
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| Challenge: | Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions . |
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