On-the-fly Table Generation

TL;DR

This paper introduces a novel approach for automatically generating relational tables in response to user queries, addressing the limitations of previous methods that only retrieved existing tables.

Contribution

It formalizes the on-the-fly table generation task and proposes a feature-based, iterative approach combining entity ranking, schema determination, and value lookup.

Findings

Effective entity ranking and schema determination improve table relevance.

Combining existing tables and knowledge bases enhances value lookup accuracy.

Approach outperforms baselines on entity-oriented query datasets.

Abstract

Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query. We introduce and address the task of on-the-fly table generation: given a query, generate a relational table that contains relevant entities (as rows) along with their key properties (as columns). This problem is decomposed into three specific subtasks: (i) core column entity ranking, (ii) schema determination, and (iii) value lookup. We employ a feature-based approach for entity ranking and schema determination, combining deep semantic features with task-specific signals. We further show that these two subtasks are not independent of each other and can assist each other in an iterative manner. For value lookup, we combine information from existing tables and a knowledge base. Using two sets of entity-oriented queries, we evaluate our approach both on the component level and on the end-to-end table generation task.