While online conversations can cover a vast amount of information in many
different formats, abstractive text summarization has primarily focused on
modeling solely news articles. This research gap is due, in part, to the lack
of standardized datasets for summarizing online discussions. To address this
gap, we design annotation protocols motivated by an
issues--viewpoints--assertions framework to crowdsource four new datasets on
diverse online conversation forms of news comments, discussion forums,
community question answering forums, and email threads. We benchmark
state-of-the-art models on our datasets and analyze characteristics associated
with the data. To create a comprehensive benchmark, we also evaluate these
models on widely-used conversation summarization datasets to establish strong
baselines in this domain. Furthermore, we incorporate argument mining through
graph construction to directly model the issues, viewpoints, and assertions
present in a conversation and filter noisy input, showing comparable or
improved results according to automatic and human evaluations.

本研究通过设计 Annotation Protocols 并使用 Argument Mining 技术，基于网络上不同形式的讨论为标准数据集，比较了各种先进的对话总结模型，在自动和人工评估中均取得了较高的结果。

ConvoSumm: 对话摘要基准以及通过论证挖掘改进的抽象式摘要

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive  Summarization with Argument Mining

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE
is a collection of 8 Korean natural language understanding (NLU) tasks,
including Topic Classification, SemanticTextual Similarity, Natural Language
Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing,
Machine Reading Comprehension, and Dialogue State Tracking. We build all of the
tasks from scratch from diverse source corpora while respecting copyrights, to
ensure accessibility for anyone without any restrictions. With ethical
considerations in mind, we carefully design annotation protocols. Along with
the benchmark tasks and data, we provide suitable evaluation metrics and
fine-tuning recipes for pretrained language models for each task. We
furthermore release the pretrained language models (PLM), KLUE-BERT and
KLUE-RoBERTa, to help reproducing baseline models on KLUE and thereby
facilitate future research. We make a few interesting observations from the
preliminary experiments using the proposed KLUE benchmark suite, already
demonstrating the usefulness of this new benchmark suite. First, we find
KLUE-RoBERTa-large outperforms other baselines, including multilingual PLMs and
existing open-source Korean PLMs. Second, we see minimal degradation in
performance even when we replace personally identifiable information from the
pretraining corpus, suggesting that privacy and NLU capability are not at odds
with each other. Lastly, we find that using BPE tokenization in combination
with morpheme-level pre-tokenization is effective in tasks involving
morpheme-level tagging, detection and generation. In addition to accelerating
Korean NLP research, our comprehensive documentation on creating KLUE will
facilitate creating similar resources for other languages in the future. KLUE
is available at this https URL

介绍了韩语自然语言理解评估标准 KLUE，涵盖 8 个韩语自然语言任务。该标准通过从多种资源语料库中构建任务和数据，并注重版权保护和伦理考虑来保证对任何人的无限制性可达。提供了合适的评估方法，预训练语言模型的微调配方以及语言模型 KLUE-BERT 和 KLUE-RoBERTa 等预训练语言模型，以帮助重现 KLUE 上的基线模型并促进未来研究。KLUE 资源还可用于其他语言的创建。

KLUE: 韩语语言理解评估

KLUE: Korean Language Understanding Evaluation

In this technical report, we present two novel datasets for image scene
understanding. Both datasets have annotations compatible with panoptic
segmentation and additionally they have part-level labels for selected semantic
classes. This report describes the format of the two datasets, the annotation
protocols, the merging strategies, and presents the datasets statistics. The
datasets labels together with code for processing and visualization will be
published at this https URL

本技术报告介绍一种用于图像场景理解的数据集，包含兼容于全景分割和部分语义类别的标注，同时提供相关统计信息和处理 / 可视化代码。