1 A -Team / M artin-Luther-Universität Halle-Wittenberg@ CLS ci S umm 20 (2020)

Abstract: This document demonstrates our groups approach to the CL-SciSumm shared task 2020. There are three tasks in CL-SciSumm 2020. In Task 1a, we apply a Siamese neural network to identify the spans of text in the reference paper best reflecting a citation. In Task 1b, we use a SVM to classify the facet of a citation.

## introduction task 1 of the cl-scisumm shared task 2020 contains two sub tasks. the document dataset for the tasks consists of multiple reference papers (rps) and for each rp a set of citing papers (cps) that all contain a citation of the original rp. for each of these citations the cited text spans and the belonging facet have been manually annotated. for task 1a the goal was to predict the cited text span for a given citation and its reference paper. in task 1b the participants had to identify what facet a cited text span belongs to, from a predefined set of facets. our team's approach utilizes a neural network for task 1a to classify pairs of (citation, reference paper sentence) as either matching or not matching. for task 1b the syntax of reference text in the form of part-of-speech n-grams is used to predict it's facet. ## related work citations play a more significant role in the scientific development than one might expect. fact is, that they help tracking the development of scientific problems and build a foundation for future research. citations spread information and are a key attribute of determining the impact of a paper or rather its value to science @xcite . there are different methods of extracting useful citations. some utelize supervised markov random fields classifiers @xcite , others modeling the link information and the citation texts @xcite , or sequence labeling with segment classification @xcite . the main goal of these approaches is to find the sentences or spans of a cp that explain some facets of the rp. because a way to see citations is as short textual parts describing some facets of the cited work. however in this document we don't need to generate or extract citations from a cited work. the citances are already given and we need to find a method to determine the sentence or span in a rp corresponding to the given citance. for this purpose it may help analyzing the aim or rethorical status of a citance like in @xcite . one work presented a classification framework based on lexically and linguistically inspired features for classifying citation functions @xcite . a different mind may think about text summarization as a helping feature to find the corresponding textual span to a given citance. fortunately the field of summerization grew to a well researched subject in the recent decades. there are several approaches to consider. some of them are topic modeling @xcite , supervised models @xcite , graph based models @xcite and neural networks @xcite . for topic modeling a probabilistic framework is used to estimate the distribution of content in the final summary. supervised models get a selection of sentences relevant for the final summary to learn on, to afterwards be able to seek the right sentences for a final summary. graph based models focus on finding the most central sentence in a graph of a text, where sentences are nodes and similarities are edges, which represents a summarizing 3 baseline