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

no code yet • 20 Feb 2024

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

no code yet • 8 Dec 2023

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

no code yet • 16 Nov 2023

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

no code yet • 15 Nov 2023

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

no code yet • 22 Sep 2023

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

no code yet • 19 May 2023

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

no code yet • 18 Apr 2023

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

no code yet • 9 Feb 2023

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

no code yet • 17 Oct 2022

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

no code yet • COLING 2022

To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i. e., PCG), for few-shot table-to-text generation.