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Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
TLDR
We address three aspects to make abstractive summarization applicable to QFS: (a)since there is no training data, we incorporate query relevance into a pre-trained abstractive model; (b) since existing abstractive models are trained in a single-document setting, we design an iterated method to embed abstractives models within the multi-document requirement of QFS. Expand
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Question Answering as an Automatic Evaluation Metric for News Article Summarization
TLDR
We present APES, Answering Performance for Evaluation of Summaries, a new metric for automatically evaluating summarization systems by querying summaries with a set of questions central to the input document. Expand
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Diffeomorphic Temporal Alignment Nets
TLDR
We propose the Diffeomorphic Temporal alignment Net (DTAN), a learning-based method for time-series joint alignment. Expand
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Interactive Extractive Search over Biomedical Corpora
TLDR
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using a powerful variant of boolean keyword queries. Expand
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Bootstrapping Relation Extractors using Syntactic Search by Examples
TLDR
The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. Expand
Question Answering as an Automatic Summarization Evaluation Metric
Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric forExpand