1 research outputs found
Summarizing and Exploring Tabular Data in Conversational Search
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.Comment: Proceedings of the 43rd International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR 2020), 202