Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations (N18-2)
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| Challenge: | Identifying the most dominant and central event of a document is useful for many applications, says a new study . identifying the most prominent event in a news article is useful in text summarization, storyline generation and text segmentation. |
| Approach: | They propose to detect the most dominant and central event of a document . central event usually has many coreferential event mentions scattered throughout document a . |
| Outcome: | The proposed task can detect the most dominant and central event in a document . it can be used for text summarization, storyline generation and text segmentation . |
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| Challenge: | Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection. |
| Approach: | They propose a cross-document coreference search task that searches for all coreferring mentions for a query event in a large document collection. |
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Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures (P18-1)
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| Challenge: | a novel approach for event coreference resolution models correlations between event chains and document topical structures. |
| Approach: | They propose a novel approach that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. |
| Outcome: | The proposed approach improves performance across a dataset of document topics . it shows that the models can identify and link event mentions that refer to the same event . |
Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News (2021.emnlp-main)
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| Challenge: | Annotating CDCR data is laborious and expensive, explaining why existing corpora are small and lack domain coverage. |
| Approach: | They use hyperlinks to extract event coreference data from online news articles . they find that models trained on small subsets of HyperCoref are highly competitive . |
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Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)
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| Challenge: | Document-level event extraction aims to extract structured information from unstructured text. |
| Approach: | They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events. |
| Outcome: | The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research. |
Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (2020.acl-main)
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| Challenge: | a recent study shows that news articles report context-informing content that is not necessarily relevant to main events. |
| Approach: | They propose to use a functional discourse structure for news articles to model news content structures . they propose to integrate system predicted news structures into the annotations . |
| Outcome: | The proposed model outperforms existing models in event coreference resolution. |
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)
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| Challenge: | Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event . |
| Approach: | They propose a method for generating a large-scale Wikipedia event coreference dataset . they use a generic approach that adapts state-of-the-art models to the cross-document setting . |
| Outcome: | The proposed method outperforms existing models and can be applied to other languages. |
Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)
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| Challenge: | Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge. |
| Approach: | They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before. |
| Outcome: | The proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of the proposed framework. |
Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
| Outcome: | The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis . |