Abstract

Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).

Highlights

  • Automatic extraction of prominent information from text has always been a core problem in language research

  • We study the task of event salience detection, to find events that are most relevant to the main content of documents

  • Our analysis shows that Kernel based Centrality Estimation (KCE) is exploiting several relations between discourse units: including script and frames (Table 5)

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Summary

Introduction

Automatic extraction of prominent information from text has always been a core problem in language research. While traditional methods mostly concentrate on the word level, researchers start to analyze higher-level discourse units in text, such as entities (Dunietz and Gillick, 2014) and events (Choubey et al, 2018). Events are important discourse units that form the backbone of our communication. Some are more central in discourse: connecting other entities and events, or providing key information of a story. It is important to be able to quantify the “importance” of events.

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