  Scientists and science journalists, among others, often need to make sense of
a large number of papers and how they compare with each other in scope, focus,
findings, or any other important factors. However, with a large corpus of
papers, it's cognitively demanding to pairwise compare and contrast them all
with each other. Fully automating this review process would be infeasible,
because it often requires domain-specific knowledge, as well as understanding
what the context and motivations for the review are. While there are existing
tools to help with the process of organizing and annotating papers for
literature reviews, at the core they still rely on people to serially read
through papers and manually make sense of relevant information.
  We present AVTALER, which combines peoples' unique skills, contextual
awareness, and knowledge, together with the strength of automation. Given a set
of comparable text excerpts from a paper corpus, it supports users in
sensemaking and contrasting paper attributes by interactively aligning text
excerpts in a table so that comparable details are presented in a shared
column. AVTALER is based on a core alignment algorithm that makes use of modern
NLP tools. Furthermore, AVTALER is a mixed-initiative system: users can
interactively give the system constraints which are integrated into the
alignment construction process.
