Map&Make: Schema Guided Text to Table Generation

TL;DR

Map&Make introduces a novel schema-guided approach for transforming complex unstructured text into interpretable tables, improving accuracy and interpretability in Text-to-Table generation tasks.

Contribution

The paper presents a new method that dissects text into propositional statements to extract schemas, enabling more accurate and nuanced table generation from unstructured text.

Findings

Significant performance improvements on Rotowire and Livesum datasets.

Effective reduction of hallucination errors in table generation.

Enhanced interpretability and structured summarization capabilities.

Abstract

Transforming dense, detailed, unstructured text into an interpretable and summarised table, also colloquially known as Text-to-Table generation, is an essential task for information retrieval. Current methods, however, miss out on how and what complex information to extract; they also lack the ability to infer data from the text. In this paper, we introduce a versatile approach, Map&Make, which "dissects" text into propositional atomic statements. This facilitates granular decomposition to extract the latent schema. The schema is then used to populate the tables that capture the qualitative nuances and the quantitative facts in the original text. Our approach is tested against two challenging datasets, Rotowire, renowned for its complex and multi-table schema, and Livesum, which demands numerical aggregation. By carefully identifying and correcting hallucination errors in Rotowire, we aim to achieve a cleaner and more reliable benchmark. We evaluate our method rigorously on a comprehensive suite of comparative and referenceless metrics. Our findings demonstrate significant improvement results across both datasets with better interpretability in Text-to-Table generation. Moreover, through detailed ablation studies and analyses, we investigate the factors contributing to superior performance and validate the practicality of our framework in structured summarization tasks.