It has been noticed that through COVID-19 greenhouse gas emissions had a
sudden reduction. Based on this significant observation, we decided to conduct
a research to quantify the impact of scientific conferences' air-travelling,
explore and suggest alternative ways for greener conferences to re-duce the
global carbon footprint. Specifically, we focused on the most popular
conferences for the Artificial Intelligence community based on their scientific
impact factor, their scale, and the well-organized proceedings towards
measuring the impact of air travelling participation. This is the first time
that systematic quantification of a state-of-the-art subject like Artificial
Intelligence takes place to define its conferencing footprint in the broader
frames of environmental awareness. Our findings highlight that the virtual way
is the first on the list of green conferences' conduction although there are
serious concerns about it. Alternatives to optimal conferences' location
selection have demonstrated savings on air-travelling CO2 emissions of up to
63.9%.

通过 COVID-19 的研究，我们定量了科学会议空中旅行对环境的影响，探索和建议更环保的会议方式，并发现了虚拟会议是最环保的方式之一，可以减少 63.9% 的二氧化碳排放。

COVID-19 对会议的重新思考 -- 人工智能会议的环境影响评估

COVID-19 Imposes Rethinking of Conferencing -- Environmental Impact  Assessment of Artificial Intelligence Conferences

Currently, no large-scale training data is available for the task of
scientific paper summarization. In this paper, we propose a novel method that
automatically generates summaries for scientific papers, by utilizing videos of
talks at scientific conferences. We hypothesize that such talks constitute a
coherent and concise description of the papers' content, and can form the basis
for good summaries. We collected 1716 papers and their corresponding videos,
and created a dataset of paper summaries. A model trained on this dataset
achieves similar performance as models trained on a dataset of summaries
created manually. In addition, we validated the quality of our summaries by
human experts.

本文提出了一种利用科学会议上的演讲视频自动生成科技论文摘要的新方法，通过收集 1716 篇论文及其对应的视频，并创建了一个摘要数据集，最终模型在该数据集上的表现与手动摘要数据集上的模型相当，并通过人工专家验证了自动生成摘要的质量。

TalkSumm: 基于学术会议演讲的科学论文摘要数据集和可扩展标注方法

TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper  Summarization Based on Conference Talks

At the heart of many scientific conferences is the problem of matching
submitted papers to suitable reviewers. Arriving at a good assignment is a
major and important challenge for any conference organizer. In this paper we
propose a framework to optimize paper-to-reviewer assignments. Our framework
uses suitability scores to measure pairwise affinity between papers and
reviewers. We show how learning can be used to infer suitability scores from a
small set of provided scores, thereby reducing the burden on reviewers and
organizers. We frame the assignment problem as an integer program and propose
several variations for the paper-to-reviewer matching domain. We also explore
how learning and matching interact. Experiments on two conference data sets
examine the performance of several learning methods as well as the
effectiveness of the matching formulations.

本文提出了一个框架来优化论文 - 审稿人分配，使用适宜性评分来测量论文和审稿人之间的成对亲和力，可以从提供的少量评分中推断出适宜性评分，将任务定义为整数规划问题，并提出了几种适用于论文和审稿人匹配问题的方案，探讨了学习和匹配相互作用的因素，对两个会议数据集进行了实验，测试了几种学习方法的性能以及匹配方法的有效性。