  We present OpinionDigest, an abstractive opinion summarization framework,
which does not rely on gold-standard summaries for training. The framework uses
an Aspect-based Sentiment Analysis model to extract opinion phrases from
reviews, and trains a Transformer model to reconstruct the original reviews
from these extractions. At summarization time, we merge extractions from
multiple reviews and select the most popular ones. The selected opinions are
used as input to the trained Transformer model, which verbalizes them into an
opinion summary. OpinionDigest can also generate customized summaries, tailored
to specific user needs, by filtering the selected opinions according to their
aspect and/or sentiment. Automatic evaluation on Yelp data shows that our
framework outperforms competitive baselines. Human studies on two corpora
verify that OpinionDigest produces informative summaries and shows promising
customization capabilities.
