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Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics ...
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Question-Based Salient Span Selection for More Controllable Text Summarization ...
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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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What is Your Article Based On? Inferring Fine-grained Provenance ...
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BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language ...
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{Z}ero-shot {L}abel-Aware {E}vent {T}rigger and {A}rgument {C}lassification ...
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Zero-shot Event Extraction via Transfer Learning: Challenges and Insights ...
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Do We Know What We Don't Know? Studying Unanswerable Questions beyond SQuAD 2.0 ...
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Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary ...
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Constrained Labeled Data Generation for Low-Resource Named Entity Recognition ...
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Cross-lingual Entity Alignment with Incidental Supervision ...
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TransOMCS: From Linguistic Graphs to Commonsense Knowledge ...
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Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection ...
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Abstract:
Identifying the key events in a document is critical to holistically understanding its important information. Although measuring the salience of events is highly contextual, most previous work has used a limited representation of events that omits essential information. In this work, we propose a highly contextual model of event salience that uses a rich representation of events, incorporates document-level information and allows for interactions between latent event encodings. Our experimental results on an event salience dataset (Liu et al., 2018) demonstrate that our model improves over previous work by an absolute 2-4 % on standard metrics, establishing a new state-of-the-art performance for the task. We also propose a new evaluation metric which addresses flaws in previous evaluation methodologies. Finally, we discuss the importance of salient event detection for the downstream task of summarization. ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/6340-is-killed-more-significant-than-fledquestion-a-contextual-model-for-salient-event-detection https://dx.doi.org/10.48448/wd2b-4108
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Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary ...
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Extending Wikification: Nominal discovery, nominal linking, and the grounding of nouns
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