Table-to-Text Generation
38 papers with code • 8 benchmarks • 6 datasets
Table-to-Text Generation is to generate a description from the structured table.
Source: Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
Benchmarks
These leaderboards are used to track progress in Table-to-Text Generation
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WebNLG
WikiBio
E2E
RotoWire
Latest papers
Automatic Logical Forms improve fidelity in Table-to-Text generation
alonsoapp/tlt
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Table-to-text systems generate natural language statements from structured data like tables.
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
These include the LogicNLG and our newly-constructed LoTNLG datasets for data insight generation, along with the FeTaQA and our newly-constructed F2WTQ datasets for query-based generation.
QTSumm: Query-Focused Summarization over Tabular Data
Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary.
TabGenie: A Toolkit for Table-to-Text Generation
We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation.
Adapting Knowledge for Few-shot Table-to-Text Generation
Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks.
Improving User Controlled Table-To-Text Generation Robustness
In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator.
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables.
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
yale-lily/loft
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Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables.
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
ramber1836/tasd
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Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.
