Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism (N19-1)
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| Challenge: | Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. |
| Approach: | They propose to use a literary theory to analyze Italo Calvino's novel Invisible Cities to leverage contextualized representations to embed each city's description and use unsupervised methods to cluster embeddings. |
| Outcome: | The proposed method can be applied to Italo Calvino’s novel Invisible Cities . authors compare results to similarity judgments generated by human readers . |
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Building Literary Corpora for Computational Literary Analysis - A Prototype to Bridge the Gap between CL and DH (L18-1)
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| Challenge: | Literature analysis using corpus-based literary analysis is slow, says aaron s. e. . literary studies researchers should focus on the research practices of literary studies, he says . |
| Approach: | et al. show litText can extract text from a 20 million word corpus using SPARQL queries. |
| Outcome: | The proposed method uses a 20 million word corpus from English, German, Spanish, French and Italian texts and an example query to identify texts where animals behave like humans as it is the case in fables. |
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)
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| Challenge: | Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) . |
| Approach: | They present a review of the literature on Natural Language Generation in Brazilian Portuguese. |
| Outcome: | The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions. |
Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation (2020.lrec-1)
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Piroska Lendvai, Sándor Darányi, Christian Geng, Moniek Kuijpers, Oier Lopez de Lacalle, Jean-Christophe Mensonides, Simone Rebora, Uwe Reichel
| Challenge: | a new study aims to detect how and when readers are experiencing engagement with a literary work . empirical literary studies and language technology are used to investigate reading absorption . |
| Approach: | They annotated user-generated book reviews with reading absorption categories . they then performed supervised binary classification of the mental state of absorption . |
| Outcome: | The proposed corpus of user-generated reviews is compared with machine learning models and a benchmark corpus. |
Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) context windows have enabled them to process inputs over 100K tokens and generate outputs of up to 10K token. |
| Approach: | They propose a multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels and an annotated fiction dataset. |
| Outcome: | The proposed framework incorporates ten metrics across the Macro, Meso, and Micro levels and is based on a human-human-AI dataset. |
Theory-Grounded Computational Text Analysis (2023.acl-short)
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| Challenge: | A broad space separates its two constituent disciplines—natural language processing and social science—which has to date been sidestepped rather than filled by applying increasingly complex computational models to problems in social science research. |
| Approach: | They argue that computational text analysis lacks organizing principles and requires organizing methods to solve problems. |
| Outcome: | The proposed approach is based on a review of 60 papers on computational text analysis. |
Literary Event Detection (P19-1)
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| Challenge: | a new dataset of literary events is presented to examine the nature of narratives . literature presents a number of challenges for existing systems, including complex narration . |
| Approach: | They propose a dataset of literary events that are depicted as taking place within the imagined space of a novel. |
| Outcome: | The proposed model achieves an F1 score of 73.9 for prestige and popularity . the best performing model achieve a score of 79.9 for prestige compared to the previous model . |
The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writing (2025.findings-acl)
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| Challenge: | Recent studies comparing AI-generated and human-authored literary texts have produced conflicting results. |
| Approach: | They hypothesize that differences in reading quality can be explained by genuine differences in how readers interpret and value literature . |
| Outcome: | The authors show that the differences in reading quality are largely explained by differences in how readers interpret and value literature, rather than by an intrinsic quality of the texts evaluated. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing? (2026.eacl-srw)
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| Challenge: | Using Neural Machine Translation, we examine whether the algorithms used in NMT have inherent inductive biases that are beneficial for most types of inputs but might harm the processing of untypical texts. |
| Approach: | They propose to use a set of measures to quantify text diversity based on its statistical properties to determine whether NMT systems struggle with maintaining the diversity of such texts. |
| Outcome: | The proposed approaches maintain the diversity and complexity of language and allow for better global planning of the output generation. |
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. |
| Approach: | They propose to modify existing models to improve detection of scenes in literary texts . they propose to use a training sample generation scheme to alleviate this problem . |
| Outcome: | The proposed model is more robust to different types of texts, while its overall performance is slightly worse than that of BERT-based models. |