  Data-to-Text Generation (D2T), a classic natural language generation problem,
aims at producing fluent descriptions for structured input data, such as a
table. Existing D2T works mainly focus on describing the superficial
associative relations among entities, while ignoring the deep comparative
logical relations, such as A is better than B in a certain aspect with a
corresponding opinion, which is quite common in our daily life. In this paper,
we introduce a new D2T task named comparative logical relation generation
(CLRG). Additionally, we propose a Comparative Logic (CoLo) based text
generation method, which generates texts following specific comparative logical
relations with contrastive learning. Specifically, we first construct various
positive and negative samples by fine-grained perturbations in entities,
aspects and opinions. Then, we perform contrastive learning in the encoder
layer to have a better understanding of the comparative logical relations, and
integrate it in the decoder layer to guide the model to correctly generate the
relations. Noting the data scarcity problem, we construct a Chinese Comparative
Logical Relation Dataset (CLRD), which is a high-quality human-annotated
dataset and challenging for text generation with descriptions of multiple
entities and annotations on their comparative logical relations. Extensive
experiments show that our method achieves impressive performance in both
automatic and human evaluations.
