Table-to-Text Generation
41 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
Most implemented papers
Prefix-Tuning: Optimizing Continuous Prompts for Generation
XiangLi1999/PrefixTuning
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ACL 2021
Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks.
Table-to-text Generation by Structure-aware Seq2seq Learning
In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table.
What Makes Good In-Context Examples for GPT-$3$?
stanfordnlp/dsp
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Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.
Neural Text Generation from Structured Data with Application to the Biography Domain
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
ukplab/emnlp2022-reasoning-aware-pretraining
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In this paper, we propose a new extended pretraining approach called Arithmetic-Based Pretraining that jointly addresses both in one extended pretraining step without requiring architectural changes or pretraining from scratch.
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.
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.
Order-Planning Neural Text Generation From Structured Data
anindyasarkarIITH/Structure_data_to_summary
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Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.
Describing a Knowledge Base
EagleW/Describing_a_Knowledge_Base
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WS 2018
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data.
