Summarizing and Exploring Tabular Data in Conversational Search
This addresses the challenge of efficiently presenting complex table information in conversational systems, though it is incremental as it builds on existing summarization methods.
The paper tackles the problem of summarizing tabular data for conversational search by generating natural language summaries, and it introduces a new dataset for this task, establishing state-of-the-art baselines.
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.