Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
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Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
Ranked #1 on Text Summarization on PubMed
We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.
Ranked #1 on Machine Translation on WMT 2017 English-Chinese
There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam.
Ranked #4 on Machine Translation on IWSLT2015 German-English
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on Text Summarization on GigaWord-10k (using extra training data)
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
Ranked #7 on Text Summarization on PubMed
As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
Ranked #3 on Question Generation on SQuAD1.1 (using extra training data)
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
Ranked #2 on Generative Question Answering on CoQA (using extra training data)
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on Intent Detection on ATIS
Summarization of speech is a difficult problem due to the spontaneity of the flow, disfluencies, and other issues that are not usually encountered in written texts.
Ranked #1 on Text Summarization on WikiHow