Named Entity Recognition Based Automatic Generation of Research Highlights (2022)

Abstract: A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers. In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.

## introduction every research domain has an overabundance of textual information, with new research articles published on a daily basis. the number of scientific papers is increasing at an exponential rate @xcite . according to reports, the number of scientific articles roughly doubles every nine years @xcite . for a researcher, keeping track of any research field is extremely difficult even in a narrow sub-field. nowadays, many publishers request authors to provide a bulleted list of research highlights along with the abstract and the full text. it can help the reader to quickly grasp the main contributions of the paper. automatic text summarization is a process of shortening a document by creating a gist of it. it encapsulates the most important or relevant information from the original text. scientific papers are generally longer documents than news stories and have a different discourse structure. additionally, there are less resources available on scholarly documents to train text summarization systems. there are two broad approaches used in automatic text summarization @xcite : extractive summarization and abstractive summarization. extractive summarization generally copies whole sentences from the input source text and combines them into a summary, discarding irrelevant sentences from the input @xcite . but recent trends use abstractive summarization which involves natural language generation to produce novel words and capture the salient information from the input text @xcite . our aim is to generate research highlights from a research paper using an abstractive approach. but an abstractive summarizer using a generative model like a pointer-generator network @xcite sometimes generates meaningless words in the output. in particular, for named entities which are multi-word strings, incorrect generation of a single word within the string (e.g., suppose instead of generating 'artificial neural network', it generates 'artificial network') may corrupt the meaning of the whole entity and its parent sentence. so we propose to perform named entity recognition (ner) on the input and treat a named entity as a single token before the input passes through the summarizer. this will avoid their fragmentation in the output. the main contributions of this paper are: ## literature survey early works on summarization of scientific articles include @xcite where an extractive summarization technique is proposed and evaluated on a small dataset of 188 scientific documents, and @xcite which exploits the rhetorical status of assertions to summarize scientific articles. more recently, lloret et al. @xcite have developed a new corpus of computer science papers from arxiv.org that contains pairs of (introduction, abstract). an approach proposed to generate abstracts from a research paper using multiple timescale model of the gated recurrent unit (mtgru) may be found in @xcite . surveys on summarization of scholarly documents appears in (altmami and menai, 2020), @xcite . generating research highlights from scientific articles is not the same as document summarization. a supervised machine learning approach is proposed in @xcite to identify relevant highlights from the full-text of a paper using a binary classifier. they have also contributed a new benchmark dataset containing author written research highlights for more than 10,000 documents. all documents adhere to a consistent discourse structure. instead of a simple binary classification of sentences as highlights or not, @xcite ) used multivariate regression methods to select the top-k most relevant sentences as research highlights. @xcite proposed a method to use appropriate weight for the named entity tagger into the swesum summarizer for swedish newspaper texts. @xcite proposed an extractive summarization technique that determines a sentence's significance based on the density of named entities. @xcite used a pointer-generator model with coverage @xcite to gener-ate research highlights from abstracts. the present work, unlike the existing ones, uses ner to avoid incorrect phrases from being generated by the decoder. note that pretrained summarization models like pegasus @xcite , t5 @xcite , and bart @xcite are trained on generic texts. fine-tuning them to a specific (e.g., scientific) domain requires large memory and computational resources; in this context, this paper provides a simpler alternative. ## methodology we use a pointer-generator network @xcite as our baseline model. while the pointergenerator model @xcite first tokenizes a document using stanford corenlp tokenizer and converts the tokens to word embeddings (trained with the model), the method we propose here performs ner on the input document and considers a named entity as a single token when training the model. we perform experiments with 4 variants: ## conclusion we applied four different deep neural models to generate research highlights from a research paper. the ner-based pointer-generator model with coverage mechanism achieved the best performance in all cases. in future, we intend to investigate in detail why ner improves the generated highlights. we also aim to ensure that the generated highlights are correct in syntax and semantics. author-written research highlights: we present a lightweight non parametric approach to generate wrinkles for 3d facial modeling and animation. our method represents a convenient approach for generating plausible facial wrinkles with low cost. our method enables the reconstruction of captured expressions with wrinkles in real time. pgm: we propose a non parametric facial modeling 3d face models from the 3d facial modeling . synthesize the 3d face expression model with 3d depth camera is considered. propose a non parametric face method to evaluate the performance of the 3d facial modeling . method is provided to evaluate the performance of the proposed approach with respect to the existing method. pgm + cov: we propose non parametric face acquisition 3d facial modeling models. face expression model is based on the texture synthesis of multiple subjects. synthesis method guarantees to 3d face face acquisition. one high quality 3d face model is studied. ner + pgm: a novel synthesis method is proposed to enhance the wrinkles using a single low cost kinect camera. the lightweight feature of the method is that it can generate plausible wrinkles using a single low cost kinect camera and one high quality 3d face model with details as the example. user specific expressions are used as blendshapes to capture facial animations in real time kinect camera and one high quality 3d face model with details. ner + pgm + cov: we present a lightweight non parametric method to generate wrinkles for 3d facial modeling and animation. the lightweight feature of the method is that it can generate plausible wrinkles. our method is low cost and convenient for common users. author-written research highlights: we propose a novel parallel 3d delaunay meshing algorithm for large scale simulations. the model information is kept during parallel triangulation process. a 3d local non delaunay mesh repair algorithm is proposed. the meshing results can be very approaching to the model boundary. the method can achieve high parallel performance and perfect scalability.