  Descriptive titles provide crucial context for interpreting tables that are
extracted from web pages and are a key component of table-based web
applications. Prior approaches have attempted to produce titles by selecting
existing text snippets associated with the table. These approaches, however,
are limited by their dependence on suitable titles existing a priori. In our
user study, we observe that the relevant information for the title tends to be
scattered across the page, and often--more than 80% of the time--does not
appear verbatim anywhere in the page. We propose instead the application of a
sequence-to-sequence neural network model as a more generalizable means of
generating high-quality titles. This is accomplished by extracting many text
snippets that have potentially relevant information to the table, encoding them
into an input sequence, and using both copy and generation mechanisms in the
decoder to balance relevance and readability of the generated title. We
validate this approach with human evaluation on sample web tables and report
that while sequence models with only a copy mechanism or only a generation
mechanism are easily outperformed by simple selection-based baselines, the
model with both capabilities outperforms them all, approaching the quality of
crowdsourced titles while training on fewer than ten thousand examples. To the
best of our knowledge, the proposed technique is the first to consider text
generation methods for table titles and establishes a new state of the art.
