  Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment
Analysis, aims to extract explicit aspect expressions from online user reviews.
We present a new framework for tackling ATE. It can exploit two useful clues,
namely opinion summary and aspect detection history. Opinion summary is
distilled from the whole input sentence, conditioned on each current token for
aspect prediction, and thus the tailor-made summary can help aspect prediction
on this token. Another clue is the information of aspect detection history, and
it is distilled from the previous aspect predictions so as to leverage the
coordinate structure and tagging schema constraints to upgrade the aspect
prediction. Experimental results over four benchmark datasets clearly
demonstrate that our framework can outperform all state-of-the-art methods.
