Information extraction from scholarly articles is a challenging task due to
the sizable document length and implicit information hidden in text, figures,
and citations. Scholarly information extraction has various applications in
exploration, archival, and curation services for digital libraries and
knowledge management systems. We present MORTY, an information extraction
technique that creates structured summaries of text from scholarly articles.
Our approach condenses the article's full-text to property-value pairs as a
segmented text snippet called structured summary. We also present a sizable
scholarly dataset combining structured summaries retrieved from a scholarly
knowledge graph and corresponding publicly available scientific articles, which
we openly publish as a resource for the research community. Our results show
that structured summarization is a suitable approach for targeted information
extraction that complements other commonly used methods such as question
answering and named entity recognition.

本文介绍 MORTY 信息提取技术，将学术论文全文压缩为以属性 - 值对为结构的文本片段，适用于数字图书馆和知识管理系统的探索、档案和策划服务，同时通过学术知识图谱和公开的科学文章，提供了一个大规模的学术数据集，研究结果表明，结构化摘要是补充其他常用方法（如问答和实体识别）的一种合适的信息提取方法。

MORTY: 从学术文章中提取目标信息的结构化摘要

MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles

In this paper, we formulate keyphrase extraction from scholarly articles as a
sequence labeling task solved using a BiLSTM-CRF, where the words in the input
text are represented using deep contextualized embeddings. We evaluate the
proposed architecture using both contextualized and fixed word embedding models
on three different benchmark datasets (Inspec, SemEval 2010, SemEval 2017) and
compare with existing popular unsupervised and supervised techniques. Our
results quantify the benefits of (a) using contextualized embeddings (e.g.
BERT) over fixed word embeddings (e.g. Glove); (b) using a BiLSTM-CRF
architecture with contextualized word embeddings over fine-tuning the
contextualized word embedding model directly, and (c) using genre-specific
contextualized embeddings (SciBERT). Through error analysis, we also provide
some insights into why particular models work better than others. Lastly, we
present a case study where we analyze different self-attention layers of the
two best models (BERT and SciBERT) to better understand the predictions made by
each for the task of keyphrase extraction.

本文提出了一种使用 BiLSTM-CRF 模型和深度语境化嵌入来解决学术论文关键词提取问题，结果表明使用语境化嵌入、BiLSTM-CRF 结构和针对特定类型文章的语境化嵌入可以达到更好的性能，并且我们还分析了各种模型的优劣。

利用上下文化嵌入的序列标注方法从学术文章中提取关键短语

Keyphrase Extraction from Scholarly Articles as Sequence Labeling using  Contextualized Embeddings

Sentence embeddings have become an essential part of today's natural language
processing (NLP) systems, especially together advanced deep learning methods.
Although pre-trained sentence encoders are available in the general domain,
none exists for biomedical texts to date. In this work, we introduce
BioSentVec: the first open set of sentence embeddings trained with over 30
million documents from both scholarly articles in PubMed and clinical notes in
the MIMIC-III Clinical Database. We evaluate BioSentVec embeddings in two
sentence pair similarity tasks in different text genres. Our benchmarking
results demonstrate that the BioSentVec embeddings can better capture sentence
semantics compared to the other competitive alternatives and achieve
state-of-the-art performance in both tasks. We expect BioSentVec to facilitate
the research and development in biomedical text mining and to complement the
existing resources in biomedical word embeddings. BioSentVec is publicly
available at this https URL

这篇论文介绍了 BioSentVec，自然语言处理中用于句子嵌入技术的一种新方法，并且在医学文本挖掘与语义相似度任务中取得了优异表现。