A natural image usually conveys rich semantic content and can be viewed from
different angles. Existing image description methods are largely restricted by
small sets of biased visual paragraph annotations, and fail to cover rich
underlying semantics. In this paper, we investigate a semi-supervised paragraph
generative framework that is able to synthesize diverse and semantically
coherent paragraph descriptions by reasoning over local semantic regions and
exploiting linguistic knowledge. The proposed Recurrent Topic-Transition
Generative Adversarial Network (RTT-GAN) builds an adversarial framework
between a structured paragraph generator and multi-level paragraph
discriminators. The paragraph generator generates sentences recurrently by
incorporating region-based visual and language attention mechanisms at each
step. The quality of generated paragraph sentences is assessed by multi-level
adversarial discriminators from two aspects, namely, plausibility at sentence
level and topic-transition coherence at paragraph level. The joint adversarial
training of RTT-GAN drives the model to generate realistic paragraphs with
smooth logical transition between sentence topics. Extensive quantitative
experiments on image and video paragraph datasets demonstrate the effectiveness
of our RTT-GAN in both supervised and semi-supervised settings. Qualitative
results on telling diverse stories for an image also verify the
interpretability of RTT-GAN.

本文提出了一种半监督的自然图像描述方法 ——RTT-GAN，利用区域感知和语言机制生成连贯的段落描述，并通过对抗训练达到建立结构性生成器和多层段落判别器之间的生成并评估技术。

循环主题转移 GAN 用于视觉段落生成

Recurrent Topic-Transition GAN for Visual Paragraph Generation

Keyphrases are useful for a variety of purposes, including summarizing,
indexing, labeling, categorizing, clustering, highlighting, browsing, and
searching. The task of automatic keyphrase extraction is to select keyphrases
from within the text of a given document. Automatic keyphrase extraction makes
it feasible to generate keyphrases for the huge number of documents that do not
have manually assigned keyphrases. A limitation of previous keyphrase
extraction algorithms is that the selected keyphrases are occasionally
incoherent. That is, the majority of the output keyphrases may fit together
well, but there may be a minority that appear to be outliers, with no clear
semantic relation to the majority or to each other. This paper presents
enhancements to the Kea keyphrase extraction algorithm that are designed to
increase the coherence of the extracted keyphrases. The approach is to use the
degree of statistical association among candidate keyphrases as evidence that
they may be semantically related. The statistical association is measured using
web mining. Experiments demonstrate that the enhancements improve the quality
of the extracted keyphrases. Furthermore, the enhancements are not
domain-specific: the algorithm generalizes well when it is trained on one
domain (computer science documents) and tested on another (physics documents).

该论文介绍了一种 Kea 关键短语提取算法新的优化方法，通过使用 Web 挖掘获得的统计关联程度作为短语间语义关系的指标，提高了提取出的关键短语的语义连贯性，不受领域限制且结果良好