Semi-supervised News Discourse Profiling with Contrastive Learning
- URL: http://arxiv.org/abs/2309.11692v1
- Date: Wed, 20 Sep 2023 23:51:34 GMT
- Title: Semi-supervised News Discourse Profiling with Contrastive Learning
- Authors: Ming Li and Ruihong Huang
- Abstract summary: News discourse profiling seeks to scrutinize the event-related role of each sentence in a news article.
We present a novel approach, denoted as Intra-document Contrastive Learning with Distillation (ICLD), for addressing the news discourse profiling task.
- Score: 27.28989421841165
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: News Discourse Profiling seeks to scrutinize the event-related role of each
sentence in a news article and has been proven useful across various downstream
applications. Specifically, within the context of a given news discourse, each
sentence is assigned to a pre-defined category contingent upon its depiction of
the news event structure. However, existing approaches suffer from an
inadequacy of available human-annotated data, due to the laborious and
time-intensive nature of generating discourse-level annotations. In this paper,
we present a novel approach, denoted as Intra-document Contrastive Learning
with Distillation (ICLD), for addressing the news discourse profiling task,
capitalizing on its unique structural characteristics. Notably, we are the
first to apply a semi-supervised methodology within this task paradigm, and
evaluation demonstrates the effectiveness of the presented approach.
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