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Table to text generation with accurate content copying.

Yang Yang¹, Juan Cao¹, Yujun Wen²

¹State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China.

Scientific Reports

|November 24, 2021

Summary

This summary is machine-generated.

This study introduces a novel transformer-based framework for table-to-text generation, improving text accuracy by integrating a copying mechanism with language modeling for better data representation.

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Area of Science:

Natural Language Processing
Artificial Intelligence
Machine Learning

Background:

Table-to-text generation aims to create fluent text from structured data.
Accurate word copying from tables is challenging, leading to out-of-vocabulary issues.
Existing methods struggle with precise data extraction and semantic relevance.

Purpose of the Study:

To develop an advanced auto-regressive framework for table-to-text generation.
To enhance the accuracy of copying words and phrases from structured data.
To improve the coherence and informativeness of generated text.

Main Methods:

An auto-regressive transformer framework combining copying and language modeling.
A word transformation method incorporating field and position information for copying.
Two auxiliary learning objectives: table-text constraint loss and copy loss.
An improved text search strategy to reduce repetition and incoherence.

Main Results:

The proposed model achieved improved BLEU and ROUGE scores on the WIKIBIO dataset.
Significant gains were observed on the ROTOWIRE dataset, with a 4.29% increase in the CO metric and 1.93 points higher BLEU.
The model demonstrated superior performance compared to baseline methods in table-to-text generation tasks.

Conclusions:

The novel framework effectively addresses challenges in accurate word copying for table-to-text generation.
The integration of copying mechanisms and auxiliary losses enhances semantic relevance and data fidelity.
The improved text search strategy contributes to more coherent and less repetitive output.