Table-to-Text Generation
37 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
Latest papers with no code
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
Table-to-Text Generation is a promising solution by facilitating the transformation of hybrid data into a uniformly text-formatted corpus.
Towards Controlled Table-to-Text Generation with Scientific Reasoning
The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information.
PixT3: Pixel-based Table To Text generation
Table-to-text generation involves generating appropriate textual descriptions given structured tabular data.
HELLaMA: LLaMA-based Table to Text Generation by Highlighting the Important Evidence
To facilitate this, we propose a search strategy to construct reasoning labels for training the table reasoner.
Effective Distillation of Table-based Reasoning Ability from LLMs
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.
ReTAG: Reasoning Aware Table to Analytic Text Generation
The task of table summarization involves generating text that both succinctly and accurately represents the table or a specific set of highlighted cells within a table.
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Specifically, tabular data and persona information are firstly represented as latent variables separately.
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
The design of our framework consists of two aspects: a prompt planner and a knowledge adapter.
Table-To-Text generation and pre-training with TabT5
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020).
Few-Shot Table-to-Text Generation with Prefix-Controlled Generator
To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i. e., PCG), for few-shot table-to-text generation.