| Challenge: | Prior work has focused on manual feature engineering and development of frameworks to test factors that influence quotability. |
| Approach: | They propose to use quotability identification as a passage ranking problem to evaluate models' performance . they use five datasets that span multiple languages and genres of literature . |
| Outcome: | The proposed model outperforms the existing model on five datasets that span multiple languages and genres of literature. |
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| Challenge: | Existing methods for quotation attribution in literary novels require varying levels of available information. |
| Approach: | They propose to train and evaluate models for character identification, coreference resolution, quotation identification and speaker attribution tasks using an annotated dataset. |
| Outcome: | The proposed model scores on speaker attribution task on the same scale as state-of-the-art models. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
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| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (2022.lrec-1)
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| Challenge: | Existing methods to extract and attribute quotations from news data are difficult and require a lot of effort. |
| Approach: | They propose a corpus of 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
| Outcome: | The proposed corpus contains 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
BERT Has More to Offer: BERT Layers Combination Yields Better Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks. |
| Approach: | They propose to combine certain layers of a BERT-based model rested on the data set and model to achieve substantially better results. |
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Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in large language models have raised concerns about reliability and trustworthiness of the models. |
| Approach: | They analyze 134 papers and introduce a taxonomy of evidence-based text generation with LLMs. |
| Outcome: | The proposed methods highlight open challenges and outline promising directions for future work. |
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (D19-1)
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| Challenge: | Existing test collections provide only document-level relevance judgments, and documents exceed the length that BERT was designed to handle. |
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| Outcome: | The proposed model aggregates sentence-level evidence to rank documents on three standard test collections. |
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. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
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BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
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Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |
RELiC: Retrieving Evidence for Literary Claims (2022.acl-long)
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| Challenge: | Literature scholars often provide evidence for claims they make in the form of quotations from a work . a novel evidence retrieval task requires a deep understanding of complex literary phenomena . |
| Approach: | They use a large-scale dataset to formulate a task of literary evidence retrieval . they implement a RoBERTa-based dense passage retriever that outperforms existing methods . |
| Outcome: | The proposed model outperforms pretrained information retrieval baselines, but there is room for improvement. |