SOTAVerified

KG-to-Text Generation

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

Papers

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Showing 1–10 of 22 papers

TitleStatusHype
MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation—0
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text GenerationCode0
Can Knowledge Graphs Simplify Text?Code1
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text—0
Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge GraphsCode0
Knowledge Graph for NLG in the context of conversational agents—0
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic ConsistencyCode0
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text GenerationCode1
EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text GenerationCode0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
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All datasetsWebNLG 2.0 (Unconstrained)WebNLG 2.0 (Constrained)AGENDAEventNarrativePathQuestionWebQuestionsWikiGraphsWebNLG (All)WebNLG (Seen)WebNLG (Unseen)ENT-DESC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodesTest perplexity27.4—Unverified
2GNNTest perplexity26.93—Unverified
3BoWTest perplexity26.65—Unverified
4UnconditionalTest perplexity25.85—Unverified