GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (2020.coling-main)
Copied to clipboard
| Challenge: | a large-scale, general-domain dataset is needed for knowledge graph-to-text generation . data collection is expensive and data-intensive, making it difficult to get good annotation . |
| Approach: | They propose to use a large-scale, general-domain dataset to generate unsupervised text from knowledge graphs. |
| Outcome: | The proposed dataset has 1.3M text and graph examples, and is a benchmark for future research . good annotation is expensive and difficult to get, and it's difficult to check quality . |
Similar Papers
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for data-to-text generation focus on single-sentence generation or long-form generation. |
| Approach: | They create a dataset that pairs Wikipedia sections with tabular data and various metadata. |
| Outcome: | The proposed dataset can generate fluent and high quality texts but struggle with coherence and factuality. |
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data. |
| Approach: | They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
| Outcome: | The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
Copied to clipboard
| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
| Approach: | They propose a new table-to-text generation dataset called TaKG that uses a set of knowledge graphs to enhance table input. |
| Outcome: | The proposed model outperforms existing models for short-text generation tasks and shows reliable performance on long-text generated across a variety of metrics. |
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
| Outcome: | The proposed method improves on open domain QA and the LAMA knowledge probe. |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
Copied to clipboard
Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. |
| Approach: | They propose a task of context-aware text generation in the scientific domain to exploit the contributions of context in generated texts. |
| Outcome: | The proposed dataset comprehensively benchmarks the efficacy of the proposed dataset in generating description and paragraph. |
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization. |
| Approach: | They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Recent improvements in KG-to-text generation are due to additional pre-training tasks . these tasks require extensive computational resources while only suggesting marginal improvements. |
| Approach: | They propose a mask structure to capture neighborhood information and a type encoder that adds a bias to the graph-attention weights depending on the connection type. |
| Outcome: | The proposed model outperforms state-of-the-art models while requiring no additional pre-training tasks. |