  Given a document and a target aspect (e.g., a topic of interest),
aspect-based abstractive summarization attempts to generate a summary with
respect to the aspect. Previous studies usually assume a small pre-defined set
of aspects and fall short of summarizing on other diverse topics. In this work,
we study summarizing on arbitrary aspects relevant to the document, which
significantly expands the application of the task in practice. Due to the lack
of supervision data, we develop a new weak supervision construction method and
an aspect modeling scheme, both of which integrate rich external knowledge
sources such as ConceptNet and Wikipedia. Experiments show our approach
achieves performance boosts on summarizing both real and synthetic documents
given pre-defined or arbitrary aspects.
