Automatically generating short summaries from users' online mental health
posts could save counselors' reading time and reduce their fatigue so that they
can provide timely responses to those seeking help for improving their mental
state. Recent Transformers-based summarization models have presented a
promising approach to abstractive summarization. They go beyond sentence
selection and extractive strategies to deal with more complicated tasks such as
novel word generation and sentence paraphrasing. Nonetheless, these models have
a prominent shortcoming; their training strategy is not quite efficient, which
restricts the model's performance. In this paper, we include a curriculum
learning approach to reweigh the training samples, bringing about an efficient
learning procedure. We apply our model on extreme summarization dataset of
MentSum posts -- a dataset of mental health related posts from Reddit social
media. Compared to the state-of-the-art model, our proposed method makes
substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding
3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative
improvements.

本研究旨在通过采用课程学习方法，结合基于 Transformers 模型的自动生成摘要技术，从 Reddit 社交媒体上的心理健康相关帖子中提取性问题，从而提高医生的阅读效率和准确性，为人们提供更及时有效的心理卫生服务。相较于目前已有的模型，本方法在 Rouge 和 Bertscore 评估指标上分别有显著的相对改进。

基于课程的思维健康在线帖子抽象摘要

Curriculum-guided Abstractive Summarization for Mental Health Online Posts

Predicting personality traits based on online posts has emerged as an
important task in many fields such as social network analysis. One of the
challenges of this task is assembling information from various posts into an
overall profile for each user. While many previous solutions simply concatenate
the posts into a long document and then encode the document by sequential or
hierarchical models, they introduce unwarranted orders for the posts, which may
mislead the models. In this paper, we propose a dynamic deep graph
convolutional network (D-DGCN) to overcome the above limitation. Specifically,
we design a learn-to-connect approach that adopts a dynamic multi-hop structure
instead of a deterministic structure, and combine it with a DGCN module to
automatically learn the connections between posts. The modules of post encoder,
learn-to-connect, and DGCN are jointly trained in an end-to-end manner.
Experimental results on the Kaggle and Pandora datasets show the superior
performance of D-DGCN to state-of-the-art baselines. Our code is available at
this https URL.

本文提出了一种动态深层图卷积网络（D-DGCN）用于预测个性特征，结合多跳结构自动学习帖子之间 的连接，通过对 Kaggle 数据集实验结果表明 D-DGCN 优于现有技术基准的性能表现。