| Challenge: | Existing studies on media bias at the article level have identified media biases but only a few have been done on article level. |
| Approach: | They propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification. |
| Outcome: | The proposed model improves both precision and recall of bias sentence identification. |
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| Challenge: | Recent work on detecting media bias at the level of individual articles is limited to single sentences. |
| Approach: | They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article. |
| Outcome: | The proposed model can detect bias at the level of individual articles and a single sentence can explain it. |
Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation (2025.acl-long)
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| Challenge: | a recent study has focused on detecting media bias in news articles . a multi-document event relation graph is used to generate a neutralized summary . |
| Approach: | They propose to generate a neutralized summary given multiple articles presenting different ideological views. |
| Outcome: | The proposed method mitigates media bias and improves content preservation. |
Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)
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| Challenge: | a recent study examined the effects of media framing on public perception and understanding of news articles. |
| Approach: | They propose to extract framing devices employed by media to assess their role in framating the narrative. |
| Outcome: | The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects. |
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
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| Challenge: | a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles. |
| Approach: | They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias . |
| Outcome: | The proposed dataset can be used for analyzing news bias and for developing and evaluating methods for news bias detection. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
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| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |
Identifying Conspiracy Theories News based on Event Relation Graph (2023.findings-emnlp)
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| Challenge: | Conspiracy theories are narratives that explains an event or situation in an irrational or malicious manner. |
| Approach: | They propose to integrate an event relation graph into conspiracy theory identification by using soft labels. |
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Context in Informational Bias Detection (2020.coling-main)
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| Challenge: | Informational bias is conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities. |
| Approach: | They explore four kinds of context for informational bias in English news articles . integrating event context improves classification performance over a strong baseline . |
| Outcome: | The best-performing model outperforms the baseline on longer sentences and sentences from politically centrist articles. |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
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| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
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| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction (2023.acl-long)
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| Challenge: | Document-level Event Causality Identification (DECI) is a sentence-level task that requires long-text understanding. |
| Approach: | They propose a document-level event causality identification model (SENDIR) that uses sparse attention to capture long-distance dependence. |
| Outcome: | The proposed model can be used to discriminate between event pairs in the same sentence or span multiple sentences. |