Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

WikiM: Metapaths based Wikification of Scientific Abstracts

In order to disseminate the exponential extent of knowledge being produced in the form of scientific publications, it would be best to design mechanisms that connect it with already existing rich repository of concepts -- the Wikipedia. Not only does it make scientific reading simple and easy (by connecting the involved concepts used in the scientific articles to their Wikipedia explanations) but also improves the overall quality of the article. In this paper, we present a novel metapath based method, WikiM, to efficiently wikify scientific abstracts -- a topic that has been rarely investigated in the literature. One of the prime motivations for this work comes from the observation that, wikified abstracts of scientific documents help a reader to decide better, in comparison to the plain abstracts, whether (s)he would be interested to read the full article. We perform mention extraction mostly through traditional tf-idf measures coupled with a set of smart filters. The entity linking heavily leverages on the rich citation and author publication networks. Our observation is that various metapaths defined over these networks can significantly enhance the overall performance of the system. For mention extraction and entity linking, we outperform most of the competing state-of-the-art techniques by a large margin arriving at precision values of 72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In order to establish the robustness of our scheme, we wikify three other datasets and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for the mention extraction and the entity linking phase

Entity Linking in Low-Annotation Data Settings

Recent advances in natural language processing have focused on applying and adapting large pretrained language models to specific tasks. These models, such as BERT (Devlin et al., 2019) and BART (Lewis et al., 2020a), are pretrained on massive amounts of unlabeled text across a variety of domains. The impact of these pretrained models is visible in the task of entity linking, where a mention of an entity in unstructured text is matched to the relevant entry in a knowledge base. State-of-the-art linkers, such as Wu et al. (2020) and De Cao et al. (2021), leverage pretrained models as a foundation for their systems. However, these models are also trained on large amounts of annotated data, which is crucial to their performance. Often these large datasets consist of domains that are easily annotated, such as Wikipedia or newswire text. However, tailoring NLP tools to a narrow variety of textual domains severely restricts their use in the real world. Many other domains, such as medicine or law, do not have large amounts of entity linking annotations available. Entity linking, which serves to bridge the gap between massive unstructured amounts of text and structured repositories of knowledge, is equally crucial in these domains. Yet tools trained on newswire or Wikipedia annotations are unlikely to be well-suited for identifying medical conditions mentioned in clinical notes. As most annotation efforts focus on English, similar challenges can be noted in building systems for non-English text. There is often a relatively small amount of annotated data in these domains. With this being the case, looking to other types of domain-specific data, such as unannotated text or highly-curated structured knowledge bases, is often required. In these settings, it is crucial to translate lessons taken from tools tailored for high-annotation domains into algorithms that are suited for low-annotation domains. This requires both leveraging broader types of data and understanding the unique challenges present in each domain