  Manually extracting relevant aspects and opinions from large volumes of
user-generated text is a time-consuming process. Summaries, on the other hand,
help readers with limited time budgets to quickly consume the key ideas from
the data. State-of-the-art approaches for multi-document summarization,
however, do not consider user preferences while generating summaries. In this
work, we argue the need and propose a solution for generating personalized
aspect-based opinion summaries from large collections of online tourist
reviews. We let our readers decide and control several attributes of the
summary such as the length and specific aspects of interest among others.
Specifically, we take an unsupervised approach to extract coherent aspects from
tourist reviews posted on TripAdvisor. We then propose an Integer Linear
Programming (ILP) based extractive technique to select an informative subset of
opinions around the identified aspects while respecting the user-specified
values for various control parameters. Finally, we evaluate and compare our
summaries using crowdsourcing and ROUGE-based metrics and obtain competitive
results.
