CNLP - NITS @ L ong S umm 2021: T ext R ank Variant for Generating Long Summaries (2021)

Abstract: The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2^{nd} Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, TextRank produced pretty decent results with minimal compute power and time. We attained 3^{rd} rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.

## introduction text summarization or summarizing large pieces of texts into comparatively smaller number of words is a challenging machine learning (ml) task that has gained significant traction in recent years. the applications are immense and diverse, from condensing and comparing legal contractual documents to summarizing medical and clinical texts. often the two approaches @xcite adopted for solving this task are: here those unmodified segments of the original text are extracted and concatenated which play the most significant role in expressing the salient sentiment of the entire text. this technique is mostly used for generating comparatively longer summaries. recently, the task of summarizing scholarly documents has grasped the attention of researchers due to the vast quantity of papers published everyday, especially in the field of machine learning. this makes it challenging for researchers and professionals to keep-up with the latest developments in the field. thus, the task of summarizing scientific papers aims not just to avoid redundancy in text and generate shorter summaries but also to cover all the salient information present in the document which often demands longer summaries. this would aid researchers to grasp the contents of the paper beyond abstract-level information without reading the entire paper. prior work on summarization of scientific documents is mostly targeted towards generation of short summaries but as mentioned before, in order to encompass all the important ideas longer summaries are required. longsumm 2021 foot_0 shared task, on the other hand, aims to encourage the researchers to focus on generating longer-form summaries for scientific papers. as mentioned before, extractive summarization methods are better accustomed for generating longer-form summaries than abstractive summarization methods, in this paper we try to summarize scientific documents using the extractive summarization technique of textrank @xcite algorithm. it is a graph-based ranking algorithm to rank the sentences in a document according to their importance in conveying the information of the document. different 'similarity' functions can be used while creating the graph which leads to varied results @xcite , therefore we chose bm25 as the similarity function. ## related works upon scrutinizing various approaches of document summarization, we have found some of the concurrent works in the field. one of these is @xcite . this work describes extractive summarization as a joint process of selection and ordering. it uses graph as its elemental part, which is used to approximate the discourse relativeness using co-reference, deverbal nouns, etc. similar works are shown by @xcite , @xcite and @xcite . other works use the google textrank algorithm @xcite to bring out the order in the text extraction. one of the works @xcite uses the modified textrank plus graph infrastructure to extract contextual information. it uses the sentence as nodes in the graph and inverse cosine similarity foot_1 to form the weights of the edges of the graph. this graph is passed as an input to the textrank algorithm which generates the required summary. similar approach is followed by @xcite which uses the power of textrank and semantic networks to form extractive summaries which bear the semantic relations. some of the works like @xcite , @xcite use capabilities of neural networks to semantically extract the information from the description and present it in human readable form. one of the works @xcite uses a joint framework of classification and selection on the textural data to form summaries. classifier architecture makes a decision as to whether a particular sentence in sequence (as selected by selector) will be the part of the membership of the summary or not, whereas the selector framework randomly selects the sentences from the description and places it in the summary. apart from these, varied approaches were adopted by the participants of the previous edition of the shared task, longsumm 2020, as mentioned in @xcite . for instance, a divide and conquer approach, dancer, was used in @xcite to summarize key sections of the paper separately and combine them through a pegasus based transformer to generate the final summary. another team (ghosh roy et al., 2020) used a neural extractive summarizer to summarize each section separately. a different team utilized the bert summarizer as shown in @xcite . the main idea was based on multi-task learning heuristic in which two tasks are optimized, namely the binary classification task of sentence selection and the section prediction of input sentences. they also suggested an abstractive summarizer based on the bart transformer that runs after the extractive summarizer. other methods were convolutional neural network (cnn) in @xcite , graph convolutional network (gcn) and graph attention network (gan) in @xcite , and unsupervised clustering in @xcite and @xcite . ## system description our approach essentially was to use the textrank algorithm @xcite to rank the sentences corresponding to their relevance to the whole text and use the most significant (highest ranked) sentences as the summary. ## conclusion and future work in this paper we targeted our efforts towards tex-trank algorithm in order to generate long extractive summaries of given scientific research papers. our approach textrank when used with bm25 similarity function, even after not being a learning algorithm, was able to achieve appreciable rouge-1 scores while remaining competitive in rouge-2 scores. as textrank is a graph-based ranking algorithm that ranks the sentences independently for each document, it requires no training, thus being compute and time efficient. although we approached the task using an algorithm which does not require training and were still able to produce substantial results, there is definitely a scope for leveraging training data to gather a general semantic structure from a collection of documents as a whole instead of working on each document independently using neural network based learning algorithms. this will definitely be our prime focus for future work in extractive text summarization. nonetheless, through our participation in longsumm 2021 we tried to optimise textrank algorithm and put it to test against other learning-based approaches of other teams and were able to pull off significant results with comparatively low machine and time requirements.