Papers with Information extraction
GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction (2025.emnlp-demos)
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| Challenge: | Existing solutions for information extraction (IE) require specialized models for different tasks or require expensive large language models. |
| Approach: | They propose a framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. |
| Outcome: | The proposed framework improves performance across diverse IE tasks and accessibility compared to LLM-based alternatives. |
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)
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Sangha Nam, Minho Lee, Donghwan Kim, Kijong Han, Kuntae Kim, Sooji Yoon, Eun-kyung Kim, Key-Sun Choi
| Challenge: | Existing studies on information extraction from unstructured texts lack a coherent evaluation of all tasks. |
| Approach: | They propose to use crowdsourcing data to develop a Korean information extraction initiative point . they propose to train and evaluate four Korean information extracting tasks using a state-of-the-art model . |
| Outcome: | The proposed model will be used to evaluate four Korean information extraction tasks using crowdsourcing data. |
TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)
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| Challenge: | Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks. |
| Approach: | They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition . |
| Outcome: | The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks. |
Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction (2023.eacl-main)
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| Challenge: | Information extraction (IE) and summarization (summarization) are closely related, but both aims to abstract the most salient information into a generated text summary. |
| Approach: | They propose to use structured IE graphs to enhance the abstractive summarization task by using cross-document IE output to incorporate an alignment loss between IE nodes and their text spans to reduce inconsistencies. |
| Outcome: | The proposed model can generate summaries that are more factual while not losing abstractiveness. |
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction (2023.findings-acl)
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| Challenge: | Empirical results show up to 11.74% absolute (20.97% relative) increase over unimodal baselines. |
| Approach: | They propose to patch the visual modality to the textual-established attribute in- formation extractor. |
| Outcome: | Empirical results show up to 11.74% absolute (29.9% relative) increase over unimodal baselines. |
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
| Outcome: | The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels. |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
Unified Structure Generation for Universal Information Extraction (2022.acl-long)
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| Challenge: | Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. |
| Approach: | They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. |
| Outcome: | The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources. |
Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)
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Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan
| Challenge: | Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance. |
| Approach: | They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance. |
| Outcome: | The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets. |
MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge (2020.emnlp-main)
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| Challenge: | Identifying task-relevant utterances improves performance at downstream medical processing. |
| Approach: | They propose a novel approach that uses task-oriented conversations to improve utterance classification over SOTA models. |
| Outcome: | The proposed model improves on a corpus of 7,000 doctor-patient conversations on 7,000 patient conversations. |
Easy-to-Hard Learning for Information Extraction (2023.findings-acl)
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| Challenge: | Existing models for information extraction (IE) use a one-stage learning strategy to extract the target structure from unstructured text data. |
| Approach: | They propose a unified easy-to-hard learning framework that mimics the human learning process by breaking down the learning process into multiple stages. |
| Outcome: | The proposed framework enables the model to acquire general IE task knowledge and improve its generalization ability on 13 out of 17 datasets. |
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |
Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments (2025.acl-long)
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| Challenge: | Specialized non-LLM NLP-based solutions lack reasoning and are not able to infer values not explicitly present in documents. |
| Approach: | They propose a novel LLM-based approach that organizes VRDs into localized semantic textual segments called semantic blocks. |
| Outcome: | The proposed approach outperforms the state-of-the-art on public VRD benchmarks by 1-3% in F1 scores and is resilient to document formats previously not encountered. |
Adaptive Ordered Information Extraction with Deep Reinforcement Learning (2023.findings-acl)
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| Challenge: | Existing methods for information extraction follow a fixed extraction order for complex tasks with multiple elements to be extracted in one instance. |
| Approach: | They propose an adaptive ordered IE paradigm to find optimal element extraction order for different instances and a reinforcement learning framework to generate optimal order dynamically. |
| Outcome: | The proposed method beats existing methods and improves on several public datasets. |
Towards Fast and Accurate Modeling for Cross-Lingual Label Projection (2026.acl-long)
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| Challenge: | Existing methods for label projection are inaccurate or slow for large-scale use. |
| Approach: | They propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective while controlling data influence during training. |
| Outcome: | The proposed method outperforms state-of-the-art methods while maintaining fast inference speed across 50+ languages. |