QTSumm: Query-Focused Summarization over Tabular Data

Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, SIMENG HAN, RUIZHE CHEN, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan

Submission Type: Regular Long Paper

Submission Track: Summarization

Submission Track 2: Natural Language Generation

Keywords: Summarization, Text Generation, Reasoning over Structured Data

TL;DR: This paper presents a new task known as query-focused table summarization and investigates the state-of-the-art text generation models for this new task.

Abstract:

People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' information needs can facilitate more efficient access to relevant data insights.
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. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics.
We investigate a set of strong baselines on QTSumm, including text generation, table-to-text generation, and large language models.
Experimental results and manual analysis reveal that the new task presents significant challenges in table-to-text generation for future research.
Moreover, we propose a new approach named ReFactor, to retrieve and reason over query-relevant information from tabular data to generate several natural language facts. Experimental results demonstrate that ReFactor can bring effective improvements to baselines by concatenating the generated facts to the model input.
Our data and code are publicly available at https://github.com/yale-nlp/QTSumm.

Submission Number: 1118