Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting their real-world usage. We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages. To address this task, we introduce a new dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we propose an automatic metric, MAFE, for factual correctness of generated descriptions. Our EntDescriptor model is equipped with strong rankers to fetch helpful passages and generate entity descriptions. Experimental result shows a good correlation (60.14) between our proposed metric and human judgments of factuality. Our rankers significantly improved the factual correctness of generated descriptions (15.95% and 34.51% relative gains in recall and precision). Finally, our ablation study highlights the benefit of combining keys and groundings.

Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.

人类视觉大脑使用三个主要成分，例如颜色，纹理和形状来检测或识别环境和物体。因此，在过去的二十年中，科学研究人员对纹理分析引起了很多关注。纹理功能可用于通勤视觉或机器学习问题的许多不同应用中。从现在开始，已经提出了许多不同的方法来对纹理进行分类。他们中的大多数将分类准确性视为应改进的主要挑战。在本文中，基于两个有效纹理描述符，共发生矩阵和局部三元模式（LTP）的组合提出了一种新方法。首先，进行基本的本地二进制模式和LTP以提取本地纹理信息。接下来，从灰度共发生矩阵中提取统计特征的子集。最后，串联功能用于训练分类器。根据准确性，在Brodatz基准数据集上评估了该性能。实验结果表明，与某些最新方法相比，提出的方法提供了更高的分类率。

纹理定义为图像中像素强度的空间结构，在整个图像或区域中定期重复，并成为图像的概念。纹理，颜色和形状是人类视觉系统使用的三个主要组件来识别图像内容。在本文中，首先，有效和更新的纹理分析操作员可以通过细节幸存。接下来，在医疗应用和疾病诊断中使用纹理分析的一些最新方法幸存下来。最后，根据准确性，数据集，应用程序等进行了比较不同的方法。结果表明，纹理特征分别或在不同特征集的关节中，例如深层，颜色或形状特征在医学图像分类中提供了很高的精度。

社交媒体的可用性和互动性使它们成为全球各地的主要新闻来源。社交媒体的普及诱惑犯罪分子通过使用诱人文本和误导性图像制作和传播假新闻来追求不道德的意图。因此，验证社交媒体新闻和发现假期至关重要。这项工作旨在分析社交媒体中文本和图像的多模态特征，以检测假新闻。我们提出了一个假新闻透露者（FNR）方法，利用转换学习，提取上下文和语义特征和对比丢失，以确定图像和文本之间的相似性。我们在两个真正的社交媒体数据集上申请了FNR。结果表明，与以前的作品相比，该方法达到了检测假新闻的更高准确性。