Multi- XS cience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles (2020)

Abstract: Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.

## introduction single document summarization is the focus of most current summarization research thanks to the availability of large-scale single-document summarization datasets spanning multiple fields, including news @xcite , nyt @xcite , newsroom @xcite , xsum @xcite ), law (bigpatent @xcite ), and even science (arxiv and pubmed @xcite ). these large-scale datasets are a necessity for modern data-hungry neural architectures (e.g. transformers @xcite ) to shine at the summarization task. the versatility of available data has proven helpful in studying different types of summarization strategies as well as both extractive and abstractive models @xcite . in contrast, research on the task of multidocument summarization (mds) -a more general scenario with many downstream applications -has not progressed as much in part due to the source 1 (abstract of query paper) ... we present an approach based on ... lexical databases and ... our approach makes use of wordnet synonymy information to .... incidentally, wordnet based approach performance is comparable with the training approach one. source 2 (cite1 abstract) this paper presents a method for the resolution of lexical ambiguity of nouns ... the method relies on the use of the wide-coverage noun taxonomy of wordnet and the notion of conceptual distance among concepts ... source 3 (cite2 abstract) word groupings useful for language processing tasks are increasingly available ... this paper presents a method for automatic sense disambiguation of nouns appearing within sets of related nouns ... disambiguation is performed with respect to wordnet senses ... source 4 (cite3 abstract) in ... word sense disambiguation... integrates a diverse set of knowledge sources ... including part of speech of neighboring words, morphological form ... summary (related work of query paper) lexical databases have been employed recently in word sense disambiguation. for example, ... [cite1] make use of a semantic distance that takes into account structural factors in wordnet ... additionally, [cite2] combines the use of wordnet and a text collection for a definition of a distance for disambiguating noun groupings. ... [cite3] make use of several sources of information ... (neighborhood, part of speech, morfological form, etc.) ... ## multi-xscience dataset we now describe the multi-xscience dataset, including the data sources, data cleaning, and the processing procedures used to construct it. we also report descriptive statistics and an initial analysis which shows it is amenable to abstractive models. ## experiments & results we study the performance of multiple state-of-theart models using the multi-xscience dataset. detailed analyses of the generation quality are also provided, including quantitative and qualitative analysis in addition to the abstractiveness study. ## extensions of multi-xscience we focus on summarization from the text of multiple documents, but our dataset could also be used for other tasks including: ## conclusion the lack of large-scale dataset has slowed the progress of multi-document summarization (mds) research. we introduce multi-xscience, a largescale dataset for mds using scientific articles. multi-xscience is better suited to abstractive summarization than previous mds datasets, since it requires summarization models to exhibit high text understanding and abstraction capabilities. experimental results show that our dataset is amenable to abstractive summarization models and is challenging for current models.