# Classification of Full Text Biomedical Documents: Sections Importance Assessment
## 1 Introduction
### Related Work  
Scientific text mining has been carried out mainly on collections of abstracts, due to their availability. In addition, studies on smaller collections of abstracts and full text articles have shown that certain information is available in the entire content of the document. Nonetheless, to date there has been no large-scale comparison of abstracts and full text articles in corpora that are similar in size to MEDLINE. In [4] the detection of text similarity in MEDLINE biomedical publications is done using full texts. The authors apply the text similarity comparison methods of the ETBLAST tool to compare the documents. They also make a comparative analysis of full text versus abstract-only, concluding that abstract-only is not necessarily predictive of full text similarity or sections therein, and asserting that full text analysis is needed for a more credible evaluation. [PERSON] et al. [5] analyze 15 million English-language scientific full text articles from Elsevier and the open-access subset of PubMed Central. The articles were published during the period from 1823 to 2016. They compare four different corpora comprising all full text articles (14,549,483 articles), full text articles that had a separate abstract (10,376,626 articles), the abstract from the full text articles (10,376,626 abstracts), and the MEDLINE corpus (16,544,511 abstracts). The study presents the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a Named Entity Recognition (NER) system, and a quantitative report on their accuracy using gold standard benchmark datasets. The authors compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE, and show that text mining of full text corpus outperforms the MEDLINE abstracts in all benchmarked cases. [PERSON] [6] compares text retrieval algorithms on MEDLINE abstracts and spans (paragraphs) within full text articles, using data from the TREC 2007 Genomics Track Evaluation. Two retrieval models are examined: BM25 and the ranking algorithm implemented in the open-source Lucene search engine. Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to an abstract-only search. However, retrievals based on spans, or paragraphs-sized segments of full text articles, consistently outperform an abstract-only search. Results suggest that the highest overall effectiveness may be achieved by combining evidence from spans and full text articles. In the literature there are articles that propose measures to evaluate the terms taking into account the structure of the document but, generally, they do not study the relevance of the sections. Thus, in [7] BM25F is applied in Information Retrieval systems, where it is used to compute the relevance score for each document depending on a fixed query. The formula takes into account the fields within a document, assigning a weight to each field. However, it is only used to calculate the term frequency of a query term, in order to create a ranking. In [8] a retrieval model which combines XPath and a vector space model for XML information retrieval is presented. Through extending the term concept to term with path, the authors introduce the concept of the structural term. A document is represented as a bag of structural terms, and a structural term is a term with a path from root node or certain node to the term itself. Lastly, in [9] the authors discuss three intrinsic problems of a specific type of Paragraph Vector (PV) model with distributed Bag-of-Words assumption (PV-DBoW) model, that restrict its performance in retrieval tasks. PV-DBoW uses the document to predict each observed word in it. The authors describe modifications to the model that make it more suitable for the information retrieval task, and show their impact through experiments and case studies.