Challenge: Existing methods to extract factual tuples from scientific text do not consider conditions.
Approach: They propose a new sequence labeling framework to jointly extract fact and condition tuples from scientific sentences.
Outcome: The proposed framework improves F1 score relative to existing methods by 4.2% and 6.2% on bioNLP2013.

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Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)

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Challenge: Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources.
Approach: They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models .
Outcome: The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models.
Biomedical Event Extraction as Sequence Labeling (2020.emnlp-main)

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Challenge: Empirical results show that BeeSL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios.
Approach: They propose a joint end-to-end neural information extraction model that recasts the task as sequence labeling and jointly models intermediate tasks via multi-task learning.
Outcome: Empirical results show that BeeSL outperforms the current best system on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1 .
Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)

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Challenge: generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks.
Approach: They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks .
Outcome: The proposed model performs on few-shot learning, slot labeling, and intent classification benchmarks.
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)

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Challenge: Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios.
Approach: They propose a novel task to find emotions and corresponding causes in unannotated emotion texts.
Outcome: The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure.
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)

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Challenge: Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature.
Approach: They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers.
Outcome: The proposed model improves on a set of English-language scientific documents.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.emnlp-main)

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Challenge: Our proposed method extracts N-ary relation tuples from scientific articles.
Approach: They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly .
Outcome: The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets.
Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)

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Challenge: a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities.
Approach: They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction.
Outcome: The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets.
A Scientific Information Extraction Dataset for Nature Inspired Engineering (2020.lrec-1)

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Challenge: Existing approaches to extract relevant biological information from scientific literature are difficult and require domain-specific knowledge.
Approach: They describe a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text.
Outcome: The proposed dataset allows for training and evaluation of Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)

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Challenge: Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective .
Approach: They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages .
Outcome: The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders.
Viable Dependency Parsing as Sequence Labeling (N19-1)

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Challenge: Existing work on dependency parsing by sequence labeling suggested that it was impractical.
Approach: They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model.
Outcome: The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.

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