Table-to-Text Generation
25 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
RotoWire
E2E
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.
Neural Text Generation from Structured Data with Application to the Biography Domain
tyliupku/wiki2bio
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EMNLP 2016
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.
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.
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.
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
We propose a novel model to separate the generation into two stages: key fact prediction and surface realization.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
ernestgong/data2text-three-dimensions
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IJCNLP 2019
To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data
h-shahidi/2birds-gen
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ACL 2020
We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively.
Variational Template Machine for Data-to-Text Generation
ReneeYe/VariationalTemplateMachine
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ICLR 2020
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
