Automatic term extraction (ATE) is a Natural Language Processing (NLP) task
that eases the effort of manually identifying terms from domain-specific
corpora by providing a list of candidate terms. As units of knowledge in a
specific field of expertise, extracted terms are not only beneficial for
several terminographical tasks, but also support and improve several complex
downstream tasks, e.g., information retrieval, machine translation, topic
detection, and sentiment analysis. ATE systems, along with annotated datasets,
have been studied and developed widely for decades, but recently we observed a
surge in novel neural systems for the task at hand. Despite a large amount of
new research on ATE, systematic survey studies covering novel neural approaches
are lacking. We present a comprehensive survey of deep learning-based
approaches to ATE, with a focus on Transformer-based neural models. The study
also offers a comparison between these systems and previous ATE approaches,
which were based on feature engineering and non-neural supervised learning
algorithms.

本文就基于深度学习和基于 Transformer 模型的 ATE 系统进行了综述，并对比了之前基于特征工程和非深度学习算法的 ATE 系统。

自动术语提取的最新进展：一项调查

The Recent Advances in Automatic Term Extraction: A survey

Recent work has shown that either (1) increasing the input length or (2)
increasing model size can improve the performance of Transformer-based neural
models. In this paper, we present a new model, called LongT5, with which we
explore the effects of scaling both the input length and model size at the same
time. Specifically, we integrated attention ideas from long-input transformers
(ETC), and adopted pre-training strategies from summarization pre-training
(PEGASUS) into the scalable T5 architecture. The result is a new attention
mechanism we call {\em Transient Global} (TGlobal), which mimics ETC's
local/global attention mechanism, but without requiring additional side-inputs.
We are able to achieve state-of-the-art results on several summarization tasks
and outperform the original T5 models on question answering tasks.

本文介绍了一种新模型 LongT5，该模型将长输入变换（ETC）的注意力机制和摘要预训练（PEGASUS）的预训练策略整合到可扩展的 T5 架构中，取得了更好的自然语言摘要和问答系统结果。