As a natural language generation task, it is challenging to generate
informative and coherent review text. In order to enhance the informativeness
of the generated text, existing solutions typically learn to copy entities or
triples from knowledge graphs (KGs). However, they lack overall consideration
to select and arrange the incorporated knowledge, which tends to cause text
incoherence.
To address the above issue, we focus on improving entity-centric coherence of
the generated reviews by leveraging the semantic structure of KGs. In this
paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based
on knowledge graphs (KGs) to improve both global and local coherence for review
generation. The proposed model learns a two-level text plan for generating a
document: (1) the document plan is modeled as a sequence of sentence plans in
order, and (2) the sentence plan is modeled as an entity-based subgraph from
KG. Local coherence can be naturally enforced by KG subgraphs through
intra-sentence correlations between entities. For global coherence, we design a
hierarchical self-attentive architecture with both subgraph- and node-level
attention to enhance the correlations between subgraphs. To our knowledge, we
are the first to utilize a KG-based text planning model to enhance text
coherence for review generation. Extensive experiments on three datasets
confirm the effectiveness of our model on improving the content coherence of
generated texts.

本文提出了一种基于知识图谱（KG）的一体化增强文本规划方法（CETP），利用 KG 的语义结构来提高生成评论的全局和局部连贯性。通过学习文档的两级文本计划，即将文档计划建模为按顺序排列的句子计划的序列以及将句子计划建模为 KG 中的基于实体的子图，进而自然地强化段落内部的实体之间的相关性和段落之间的相关性。通过在三个数据集上的广泛实验证明了我们的模型在提高生成文本内容连贯性方面的有效性。

基于知识的连贯增强文本规划的评论生成

Knowledge-based Review Generation by Coherence Enhanced Text Planning

Building effective text generation systems requires three critical
components: content selection, text planning, and surface realization, and
traditionally they are tackled as separate problems. Recent all-in-one style
neural generation models have made impressive progress, yet they often produce
outputs that are incoherent and unfaithful to the input. To address these
issues, we present an end-to-end trained two-step generation model, where a
sentence-level content planner first decides on the keyphrases to cover as well
as a desired language style, followed by a surface realization decoder that
generates relevant and coherent text. For experiments, we consider three tasks
from domains with diverse topics and varying language styles: persuasive
argument construction from Reddit, paragraph generation for normal and simple
versions of Wikipedia, and abstract generation for scientific articles.
Automatic evaluation shows that our system can significantly outperform
competitive comparisons. Human judges further rate our system generated text as
more fluent and correct, compared to the generations by its variants that do
not consider language style.

通过端到端训练的两步生成模型，即首先由句子级内容规划器决定涵盖的关键词组和所需的语言风格，然后由表面实现解码器生成相关和连贯的文本，可以显著优于现有竞争模型，在 Reddit 的说服性论证、维基百科的正常和简单版本的段落生成以及科学文章的摘要生成等任务中得到验证。

神经文本生成的句子级内容规划和风格规范

Sentence-Level Content Planning and Style Specification for Neural Text  Generation

Data-to-text generation can be conceptually divided into two parts: ordering
and structuring the information (planning), and generating fluent language
describing the information (realization). Modern neural generation systems
conflate these two steps into a single end-to-end differentiable system. We
propose to split the generation process into a symbolic text-planning stage
that is faithful to the input, followed by a neural generation stage that
focuses only on realization. For training a plan-to-text generator, we present
a method for matching reference texts to their corresponding text plans. For
inference time, we describe a method for selecting high-quality text plans for
new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our
results demonstrate that decoupling text planning from neural realization
indeed improves the system's reliability and adequacy while maintaining fluent
output. We observe improvements both in BLEU scores and in manual evaluations.
Another benefit of our approach is the ability to output diverse realizations
of the same input, paving the way to explicit control over the generated text
structure.

将数据转文本生成过程拆分为符号文本计划和神经生成两个阶段，通过匹配参考文本和选择高质量文本计划提高系统的可靠性和适用性，同时保持流畅的输出。在 WebNLG 基准测试中得到了 BLEU 得分和人工评估的改善，并能够输出同一输入的多种不同的实现，为生成文本结构的显式控制铺平了道路。