Query-based text summarization is an important real world problem that
requires to condense the prolix text data into a summary under the guidance of
the query information provided by users. The topic has been studied for a long
time and there are many existing interesting research related to query-based
text summarization. Yet much of the work is not systematically surveyed. This
survey aims at summarizing some interesting work in query-based text
summarization methods as well as related generic text summarization methods.
Not all taxonomies in this paper exist the related work to the best of our
knowledge and some analysis will be presented.

研究旨在对基于查询和通用文本汇总方法的有趣工作进行概述，并提供相关领域的分类分析。

基于查询的文本摘要综述

Survey of Query-based Text Summarization

We propose a deep learning approach for user-guided image colorization. The
system directly maps a grayscale image, along with sparse, local user "hints"
to an output colorization with a Convolutional Neural Network (CNN). Rather
than using hand-defined rules, the network propagates user edits by fusing
low-level cues along with high-level semantic information, learned from
large-scale data. We train on a million images, with simulated user inputs. To
guide the user towards efficient input selection, the system recommends likely
colors based on the input image and current user inputs. The colorization is
performed in a single feed-forward pass, enabling real-time use. Even with
randomly simulated user inputs, we show that the proposed system helps novice
users quickly create realistic colorizations, and offers large improvements in
colorization quality with just a minute of use. In addition, we demonstrate
that the framework can incorporate other user "hints" to the desired
colorization, showing an application to color histogram transfer. Our code and
models are available at this https URL

本研究提出了一种基于深度学习的图像上色系统，在输入灰度图像和用户指定信息的情况下，利用卷积神经网络将其映射至彩色图像，并对其进行上色。该系统利用大规模数据集对低级和高级语义信息进行学习，根据现有输入向用户推荐可能的颜色，通过单次前馈传递实现实时操作。实验证明，即使使用随机模拟的用户输入，该系统也能够有效地帮助用户快速创建逼真的彩色图像。