Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation
This addresses the lack of subjectivity in table-to-text generation for natural language processing applications, though it is incremental as it builds on existing sequence-to-sequence and LLM methods.
The paper tackles the problem of generating text from tables that includes both objective facts and subjective interpretations, introducing the Ta2TS dataset with 3849 instances and showing that fine-tuned models achieve 85.15% BERTScore and 26.28% Meteor score, performing close to prompted large language models.
Table-to-text generation, a long-standing challenge in natural language generation, has remained unexplored through the lens of subjectivity. Subjectivity here encompasses the comprehension of information derived from the table that cannot be described solely by objective data. Given the absence of pre-existing datasets, we introduce the Ta2TS dataset with 3849 data instances. We perform the task of fine-tuning sequence-to-sequence models on the linearized tables and prompting on popular large language models. We analyze the results from a quantitative and qualitative perspective to ensure the capture of subjectivity and factual consistency. The analysis shows the fine-tuned LMs can perform close to the prompted LLMs. Both the models can capture the tabular data, generating texts with 85.15% BERTScore and 26.28% Meteor score. To the best of our knowledge, we provide the first-of-its-kind dataset on tables with multiple genres and subjectivity included and present the first comprehensive analysis and comparison of different LLM performances on this task.