KG-to-Text Generation
17 papers with code • 11 benchmarks • 9 datasets
Knowledge-graph-to-text (KG-to-text) generation aims to generate high-quality texts which are consistent with input graphs.
Description from: JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Benchmarks
These leaderboards are used to track progress in KG-to-Text Generation
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WebQuestions
WebNLG
AGENDA
WikiGraphs
XAlign
EventNarrative
Most implemented papers
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
thu-coai/JointGT
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Findings (ACL) 2021
Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset
deepmind/deepmind-research
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NAACL (TextGraphs) 2021
We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.
EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation
We also evaluate two types of baseline on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation
acolas1/GAP_COLING2022
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COLING 2022
Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance.
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency
Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG.
Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
In any system that uses structured knowledge graph (KG) data as its underlying knowledge representation, KG-to-text generation is a useful tool for turning parts of the graph data into text that can be understood by humans.
Can Knowledge Graphs Simplify Text?
acolas1/kgsimple
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Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG.
