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Ramesh Nallapati
Researcher at Amazon.com
Publications - 97
Citations - 10853
Ramesh Nallapati is an academic researcher from Amazon.com. The author has contributed to research in topics: Topic model & Automatic summarization. The author has an hindex of 31, co-authored 88 publications receiving 9046 citations. Previous affiliations of Ramesh Nallapati include IBM & Stanford University.
Papers
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Proceedings ArticleDOI
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
TL;DR: This paper proposed several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-toword structure, and emitting words that are rare or unseen at training time.
Proceedings ArticleDOI
Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora
TL;DR: Labeled LDA is introduced, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags that allows Labeled LDA to directly learn word-tag correspondences.
Posted Content
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
TL;DR: This paper proposed several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time.
Proceedings Article
SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents
TL;DR: SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Proceedings Article
Multi-instance Multi-label Learning for Relation Extraction
TL;DR: This work proposes a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their labels using a graphical model with latent variables that performs competitively on two difficult domains.