ARTU / TU W ien and Artificial Researcher@ L ong S umm 20 (2020)

Abstract: In this paper, we present our approach to solve the LongSumm 2020 Shared Task, at the 1st Workshop on Scholarly Document Processing. The objective of the long summaries task is to generate long summaries that cover salient information in scientific articles. The task is to generate abstractive and extractive summaries of a given scientific article. In the proposed approach, we are inspired by the concept of Argumentative Zoning (AZ) that de- fines the main rhetorical structure in scientific articles. We define two aspects that should be covered in scientific paper summary, namely Claim/Method and Conclusion/Result aspects. We use Solr index to expand the sentences of the paper abstract. We formulate each abstract sentence in a given publication as query to retrieve similar sentences from the text body of the document itself. We utilize a sentence selection algorithm described in previous literature to select sentences for the final summary that covers the two aforementioned aspects.

## introduction scientific publications differ in structure, format, and style when compared with other text works (e.g. news articles). as a result, summarizing scientific articles is a challenging task since exploiting known summarization techniques, like those employed by the mead system @xcite , that work well for general texts, cannot work well when applied to scientific articles. summarization of texts, scientific of not, has been of interest to researchers since the 1950s, when peter luhn published his paper "the automatic creation of literature abstracts". one of the most notable approaches to the summarization of scientific papers introduces the concept of argumentative zoning @xcite which refers to the examination of the argumentative status of sentences in scientific articles and their assignment to specific argumentative categories (i.e. zones). building on this work, further research has been done to design automatic techniques for argumentative zoning @xcite @xcite . a different and more recent approach to summarization makes use of citations to construct a summary of the main concepts and contributions in scientific articles @xcite @xcite . starting from this research, since 2014, a series of pilot and shared tasks on summarization have been organized with some regularity. the cl-scisumm shared tasks @xcite @xcite require task participants to map citation sentences from a given scientific publication to reference sentences in the original articles, and generate a summary from those sentences using predefined facets. this year, 2020, the cl-scisumm shared task has introduced longsumm as a new challenge @xcite . in this challenge, given a scientific paper, it is required to generate the extractive and the abstractive summaries of the paper. in this paper, we report on our approach to solving the longsumm challenge of the cl-scisumm shared task. we are proposing a summarization technique that builds on the concept of argumentative zoning @xcite and uses a solr index to expand the abstract of a scientific article to obtain a summary of it. in this approach we extract the main aspects of the scientific articles into a summary by defining zones of interest in the article: claims, methods, results and conclusions. from our own experience we have observed that these aspects are best to form an informative summary for the researcher. the rest of the paper is organized as follows: section 2 discusses the components of the processing pipeline that constitute our approach in more detail. in section 3 we describe our experimental settings and evaluation results for individual components of our pipeline. finally, we discuss the conclusion and potential direction of future work in section 4. ## approach in a nutshell, our approach to solve the longsumm shared task is to extract an article summary by expanding its abstract. that is, for each sentence in the article's abstract we extract relevant paragraphs from the same article employing a combination of language model-based retrieval and then classification to find similar paragraphs out of which, in a final step, we select certain sentences to be part of the article summary. figure 1 shows an overview of our proposed approach. in a first phase, we convert the pdf papers to an xml format using the grobid foot_0 pdf parser (the box marked with 1 in figure 1 ). the output of this step is split into the abstract of the article and the article body. the latter is processed by a rulebased annotation module (2) which will assign specific category labels to the individual paragraphs. the paragraphs and their category annotations are fed into a solr foot_1 index (3). the abstract sentences are classified into two categories, claim/method and conclusion/result (block 5a in figure 1 ). in the next phase, the abstract sentences are sent as queries to the solr index in order to find paragraphs that are similar to them (4). we note that the abstract sentence annotations are not used in this phase. these annotations will be used later in the last phase of our summarization process. the sentence, paragraph pairs that are the output of the solr retrieval step are now passed to a classifier which will decide if the two are similar to each other or not (5b). pairs that are classified as similar are used to extract the final paper summary by applying the sentence selection algorithm mentioned in @xcite . the details for each module are discussed in the following subsections. ## experiments and result in this section, we discuss the experimental setup and the evaluation results for the annotation tasks and the classification modules described in the previous section. we close the section with an evaluation of the final summarization step in our approach. ## conclusion and future work we have presented an approach to solve the long-summ'20 shared task. our aim was to extract the essence of a scientific paper in order to give the reader a quick overview of its central aspects: the authors' hypothesis (the claim), the method used test the hypothesis and the outcome of the experiment. in @xcite , the authors propose to create indicative-informative summaries by observing how professional abstractors generated scientific summaries, and thereafter manually align the professional abstract with the source data. an indicative abstract is composed of the topic of the document, i.e. the essence of the paper. since we did not have access to professional abstractors, we have decided to use different approach to derive the essence of a paper. we assumed that the existing abstract is a very short summary focusing on the why, the how, and on the general outcome of a scientific work, which we organize into claims, methods, results and conclusions using the argumentative zoning schema. in a first run we asked students to label abstract sentences with these four categories. in order to align the labeled abstract sentences with the body text of an article, we have used a language model where each abstract sentence is converted into a query and the system retrieves similar sentences from the body text of the same article. in a second annotation task we have asked students to assess if the retrieved sentence is relevant to the abstract, and if it could be considered to be assigned to same category as the abstract sentence. finally, we utilize the sentence selection method described by @xcite to form the final summary. we have not yet explored the full potential of the algorithm due to two main factors: as future work, we plan to address these these issues by clarifying the annotation guidelines and test the anotators' performance accross different scientific fields. other future work is to investigate how changes in the round-robin sentence selection algorithm may impact the quality of the extracted summaries. more specifically, we may look at recomputing the scores of the sentences in the clusters after each sentence selection, where scores could reflect not only relevance to the abstract sentence, but also novelty compared to the other sentences in the same cluster, as well as the clusters of other abstract sentences of the same category.