1 code implementation • 10 Oct 2023 • Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser
We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer.
1 code implementation • 6 Jun 2023 • Nianlong Gu, Richard H. R. Hahnloser
We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords.
1 code implementation • 19 May 2023 • Yingqiang Gao, Jessica Lam, Nianlong Gu, Richard H. R. Hahnloser
This implicit nature of conclusion positions makes the automatic segmentation of scientific abstracts into premises and conclusions a challenging task.
no code implementations • 17 Nov 2022 • Sumit Kumar, B. Anshuman, Linus Ruettimann, Richard H. R. Hahnloser, Vipul Arora
The key objective of this work is to learn useful embeddings associated with high performance in downstream event detection tasks when labeled data is scarce and the audio events of interest (songbird vocalizations) are sparse.
1 code implementation • 14 Nov 2022 • Nianlong Gu, Richard H. R. Hahnloser
Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript.
1 code implementation • 2 Dec 2021 • Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser
The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context.
Ranked #1 on Citation Recommendation on FullTextPeerRead
1 code implementation • ACL 2022 • Nianlong Gu, Elliott Ash, Richard H. R. Hahnloser
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history.
Ranked #1 on Extractive Text Summarization on GovReport
no code implementations • ACL 2020 • Onur Gökçe, Jonathan Prada, Nikola I. Nikolov, Nianlong Gu, Richard H. R. Hahnloser
Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited.
no code implementations • ACL 2020 • Yingqiang Gao, Nikola I. Nikolov, Yuhuang Hu, Richard H. R. Hahnloser
We explore the suitability of self-attention models for character-level neural machine translation.
1 code implementation • 1 Feb 2020 • Thanuja D. Ambegoda, Julien N. P. Martel, Jozef Adamcik, Matthew Cook, Richard H. R. Hahnloser
Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale.
1 code implementation • LREC 2020 • Nikola I. Nikolov, Richard H. R. Hahnloser
Abstractive summarization typically relies on large collections of paired articles and summaries.
1 code implementation • RANLP 2019 • Nikola I. Nikolov, Alessandro Calmanovici, Richard H. R. Hahnloser
We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality.
1 code implementation • RANLP 2019 • Nikola I. Nikolov, Richard H. R. Hahnloser
We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers.
1 code implementation • WS 2018 • Nikola I. Nikolov, Yuhuang Hu, Mi Xue Tan, Richard H. R. Hahnloser
Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs.
3 code implementations • 24 Apr 2018 • Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser
Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles.