Abstract: 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.

Introduction: One of the crucial tasks for researchers to carry out scientific investigations is to detect existing ideas that are related to their research topics. Research ideas are usually documented in scientific publications. Normally, there is one main idea stated in the abstract, explicitly presenting the aim of the paper. There are also other sub-ideas distributed across the entire paper. As the growth rate of scientific publication has been rising dramatically, researchers are overwhelmed by the explosive information. It is almost impossible to digest the ideas contained in the documents emerged everyday. Therefore, computer assisted technologies such as document summarization are expected to play a role in condensing information and providing readers with more relevant short texts. Unlike document summarization from news circles, where the task is to identify centroid sentences BIBREF0 or to extract the first few sentences of the paragraphs BIBREF1 , summarization of scientific articles involves extra text processing stage BIBREF2 . After highest ranked texts are extracted, rhetorical status analysis will be conducted on the selected sentences. Rhetorical sentence classification, also known as argumentative zoning (AZ) BIBREF3 , is a process of assigning rhetorical status to the extracted sentences. The results of AZ provide readers with general discourse context from which the scientific ideas could be better linked, compared and analyzed. For example, given a specific task, which sentences should be shown to the reader is related to the features of the sentences. For the task of identifying a paper's unique contribution, sentences expressing research purpose should be retrieved with higher priority. For comparing ideas, statements of comparison with other works would be more useful. Teufel et. al. BIBREF2 introduced their rhetorical annotation scheme which takes into account of the aspects of argumentation, metadiscourse and relatedness to other works. Their scheme resulted seven categories of rhetorical status and the categories are assigned to full sentences. Examples of human annotated sentences with their rhetorical status are shown in Table. TABREF2 . The seven categories are aim, contrast, own, background, other, basis and textual. Analyzing the rhetorical status of sentences manually requires huge amount of efforts, especially for structuring information from multiple documents. Fortunately, computer algorithms have been introduced to solve this problem. With the development of artificial intelligence, machine learning and computational linguistics, Natural Language Processing (NLP) has become a popular research area BIBREF4 , BIBREF5 . NLP covers the applications from document retrieval, text categorization BIBREF6 , document summarization BIBREF7 to sentiment analysis BIBREF8 , BIBREF9 . Those applications are targeting different types of text resources, such as articles from social media BIBREF10 and scientific publications BIBREF2 . There are several approaches to tackle these tasks. From machine learning prospective, text can be analysed via supervised BIBREF2 , semi-supervised BIBREF11 and unsupervised BIBREF12 algorithms. Document summarization from social media and news circles has received much attention for the past decades. Those problems have been addressed from many angles, one of which is feature extraction and representation. At the early stage of document summarization, features are usually engineered manually. Although the hand-crafted features have shown the ability for document summarization and sentiment analysis BIBREF13 , BIBREF9 , there are not enough efficient features to capture the semantic relations between words, phrases and sentences. Moreover, building a sufficient pool of features manually is difficult, because it requires expert knowledge and it is time-consuming. Teufel et. al. BIBREF2 have built feature pool of sixteen types of features to classify sentences, such as the position of sentence, sentence length and tense. Widyantoro et. al. used content features, qualifying adjectives and meta-discourse features BIBREF14 to explore AZ task. It took efforts to engineer these features and it is also time consuming to optimize the combination of the entire features. With the advent of neural networks BIBREF15 , it is possible for computers to learn feature representations automatically. Recently, word embedding technique BIBREF16 has been widely used in the NLP community. There are plenty of cases where word embedding and sentence representations have been applied to short text classification BIBREF17 and paraphrase detection BIBREF18 . However, the effectiveness of this technique on AZ needs further study. The research question is, is it possible to extract word embeddings as features to classify sentences into the seven categories mentioned above using supervised machine learning approach?

Question: What metric is considered?
Answer: Precision, recall and F-measure.