TL;DR
This paper introduces a new dataset and methods for generating natural language summaries of tables to improve conversational search, enabling better exploration of complex tabular data.
Contribution
It presents a novel dataset for table summarization in conversational search and develops baseline systems for automatic summarization.
Findings
Crowdsourced dataset of table summaries created.
Baseline models achieve competitive performance.
Identified challenges and future directions in table summarization.
Abstract
Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset. It includes annotated table summaries, which not only answer questions but also help people explore other information in the table. We utilize this dataset to develop automatic table summarization systems as SOTA baselines. Based on the experimental results, we identify challenges and point out future research directions that this resource will support.
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