  In comparison with document summarization on the articles from social media
and newswire, argumentative zoning (AZ) is an important task in scientific
paper analysis. Traditional methodology to carry on this task relies on feature
engineering from different levels. In this paper, three models of generating
sentence vectors for the task of sentence classification were explored and
compared. The proposed approach builds sentence representations using learned
embeddings based on neural network. The learned word embeddings formed a
feature space, to which the examined sentence is mapped to. Those features are
input into the classifiers for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on the
Argumentative-Zoning (AZ) annotated articles. The results showed that simply
averaging the word vectors in a sentence works better than the paragraph to
vector algorithm and by integrating specific cuewords into the loss function of
the neural network can improve the classification performance. In comparison
with the hand-crafted features, the word2vec method won for most of the
categories. However, the hand-crafted features showed their strength on
classifying some of the categories.
