  Pagination - the process of determining where to break an article across
pages in a multi-article layout is a common layout challenge for most
commercially printed newspapers and magazines. To date, no one has created an
algorithm that determines a minimal pagination break point based on the content
of the article. Existing approaches for automatic multi-article layout focus
exclusively on maximizing content (number of articles) and optimizing aesthetic
presentation (e.g., spacing between articles). However, disregarding the
semantic information within the article can lead to overly aggressive cutting,
thereby eliminating key content and potentially confusing the reader, or
setting too generous of a break point, thereby leaving in superfluous content
and making automatic layout more difficult. This is one of the remaining
challenges on the path from manual layouts to fully automated processes that
still ensure article content quality. In this work, we present a new approach
to calculating a document minimal break point for the task of pagination. Our
approach uses a statistical language model to predict minimal break points
based on the semantic content of an article. We then compare 4 novel candidate
approaches, and 4 baselines (currently in use by layout algorithms). Results
from this experiment show that one of our approaches strongly outperforms the
baselines and alternatives. Results from a second study suggest that humans are
not able to agree on a single "best" break point. Therefore, this work shows
that a semantic-based lower bound break point prediction is necessary for ideal
automated document synthesis within a real-world context.
