Genre Identification and the Compositional Effect of Genre in Literature (C18-1)
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| Challenge: | Literature is artistic and conveys complex themes over the course of very long narratives. |
| Approach: | They propose a method which can work with large literary corpus of texts . they propose 'gutenberg' dataset to perform Genre Identification . |
| Outcome: | The proposed methods improve results in a literature-based task with 200,000 words of literature . the Gutenberg dataset is used to model literary classifications with a high level of fidelity . |
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| Challenge: | a concept of move structure has been overlooked in genre analysis, but it is not widely used in natural language processing. |
| Approach: | They propose to incorporate move structure into a neural architecture for automatic genre identification. |
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Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre (2022.coling-1)
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| Challenge: | linguistically motivated features are used to classify paragraph-level text into fiction and non-fiction genres. |
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Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
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Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task (2022.lrec-1)
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| Challenge: | Genre identification is a kind of non-topic text classification. genre is defined as a functional space. |
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Chapter Captor: Text Segmentation in Novels (2020.emnlp-main)
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| Challenge: | Using a hybrid approach, we identify chapter boundaries in novels . chapter boundaries are typically denoted by formatting conventions such as page breaks, white-space, chapter numbers, and titles. |
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| Challenge: | Existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies and contain strong layout and stylistic biases. |
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Investigating the Working of Text Classifiers (C18-1)
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| Challenge: | Text classification is one of the most widely studied tasks in natural language processing. |
| Approach: | They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy . |
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Assessing the State of the Art in Scene Segmentation (2025.naacl-long)
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| Challenge: | Recent advances in scene segmentation have made it difficult to detect scenes in literary texts. |
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Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
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CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis (2024.lrec-main)
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| Challenge: | XIX and XX century English novels annotated automatically contain 41,715 labeled clauses . a new approach to analyze novels based on clauses captures structural patterns within books, as well as qualitative differences between them. |
| Approach: | They propose to use a corpus of XIX and XX century English novels annotated automatically to study stories as sequences of eventive, subjective and contextual information. |
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