Extractive Research Slide Generation Using Windowed Labeling Ranking (2021)

Abstract: Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.

## introduction it has become common practice for researchers to use slides as a visual aid in presenting research findings and innovations. such slides usually contain bullet points that the researchers believe to be important to show. these bullet points serve both as a reminder to the speaker (when he/she is presenting) and summaries for audiences to understand. manually creating a set of high-quality slides from an academic paper is time-consuming. we propose a method that automatically selects salient sentences that could be included into the slides, with the purpose of reducing the time and effort for slide generation. the main challenge for solving this problem is to accurately extract the main points from an academic paper. this is due to the limitations of existing methods to fully encode semantics of sentences and the implicit relations between sentences. here, we propose an extractive summarizer that identifies the best sentence in a set of consecutive sentence windows. the selection process depends on importance and novelty of the sentence that is modeled by the neural networks. the selected sentences and their frequent noun phrases are then structured in a layered format to make the bullet points of the slides. presentation slides are usually created with multiple bullet points organized in a multi-level hierarchical structure, usually with phrases summarizing high level topics at the first level and bullets at the second and other levels for further clarification or details. statistical analysis on our training data set shows that more than 92% of the bullets are in the first and second level and only 8% are in the third layer. therefore, we built our presentations in two level bullet points only. our contribution is threefold. ## related work summarizing scholarly articles in presentation slides is different from standard text summarization @xcite , which focuses on generating a paragraph of free text summary out of a longer document. automatic slide generation can be done by first extracting salient sentences in a hierarchical order and grouping them into slides that are sequentially aligned with the original paper. ppsgen @xcite was a framework that automatically generated presentation slides from scientific papers. they applied support vector regressor and integer linear programming (ilp) to rank and select important sentences. @xcite generate slides by extracting phrases from papers and learning the hierarchical relationship between pairs of phrases to build the structure of bullet points. their model is trained on a small set of 175 paper-slide pairs. the slideseer @xcite project crawled more than 10,000 paper-slide pairs using the google apis to search for the slide of papers using their title as a search query. the full set of data is not publicly available (only 20 pairs are available). compared with previous works, our model is trained and tested on a relatively large set of 5000 paper-slide pairs and the dataset will be publicly available for future works. there had been some work on the alignment of presentations slides to the article sections @xcite @xcite . summarunner @xcite is a neural extractive summarizer that treats the summarization task as a sequence labeling problem. sum-marunner was evaluated on cnn/daily mail corpus, which contains news articles that are shorter than research papers. we improve upon the sum-marunner model for the summarization of scientific papers. ## data producing a large dataset for summarization of scientific documents is challenging and requires domain experts to make the summaries. the latest cl-scisumm 2018 summarization task contains only 40 nlp papers with human-annotated reference summaries. recently, scisummnet @xcite expanded the cl-scisumm to 1000 scientific articles. talksum @xcite summarizes scientific articles based on the transcripts of the presentation talks at conferences. using presentation slides made by the authors is promising for the training of deep neural summarization models as more conferences are providing slides with papers. we crawled more than 5,000 paper-slide pairs from a manually curated list of websites, e.g., usenix.org and aclweb.org. grobid @xcite is used to get metadata and the body of the text from scientific papers in pdf format. presentations are transformed form pdf or ppt format to xml by apache tika foot_0 . the tika xml files are divided into pages and the text is extracted using optical character recognition (ocr) tools. most venues of papers in our dataset are in computational linguistics, system, and system security. in our dataset, there are on average 35 pages of slide per presentation and 8 lines of text per slide page. the majority (75%) of papers are published between 2013 and 2019. we used this dataset (called ps5k) to train summarization models to identify important parts of the input document at the sentence level. ## method generating slides requires identifying important sentences of the input scientific article and consists of three main steps. the first is to label salient sentences in the paper that are literally similar to corresponding slides. the second is to train the model to rank sentences and the final step selects salient sentences based on the predicted scores, size of the summary and the length of the sentences. afterwards, frequent noun phrases are extracted from the selected sentences to shape the hierarchical structure of the bullet points. the architecture of our model is shown in figure 1 . ## experiments and results we estimated the parameters of our model on ps5k. we split the dataset into training, validation, and testing set, each consisting of 4500, 250, and 250 pairs, respectively. we experimented with different window sizes and found that a window size of @xmath0. the stanford corenlp (manning et al., 2014) is used to tokenize and lemmatize sentences to the constituent tokens and to extract noun phrases. glove @xcite 50-dimensional vectors are used to initialize the word embeddings. with the adadelta optimizer and a learning rate of 0.1, we trained for 50 epochs. the sentences are truncated or padded to have 50 tokens (only 8% sentences consist of more than 50 tokens). similarly, we adopt a fixed document size of 500 sentences (only 3.5% of documents in our dataset have more than 500 sentences). we used the standard rouge score @xcite to evaluate the summaries. the rouge scores for summaries are tabulated in ## conclusion we create and make available ps5k, which is a large slide-paper dataset consisting of 5,000 scientific articles and corresponding manually made slides. this dataset can be used for scientific document summarization and slide generation. we used state of the art extractive summarization methods to summarize scientific articles. our results show that distributing the positive labels across all sections of a scientific paper, in contrast to summarization methods for news articles, considerably improves performance.