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Found 21 papers, 6 papers with code
Data Augmentation for Low-Resource Dialogue Summarization
We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization.
A Thorough Evaluation of Task-Specific Pretraining for Summarization
Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al., 2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al., 2020).
$μ$PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge
Cross-lingual summarization consists of generating a summary in one language given an input document in a different language, allowing for the dissemination of relevant content across speakers of other languages.
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content.
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty.
OpineSum: Entailment-based self-training for abstractive opinion summarization
In this work, we present a novel self-training approach, OpineSum, for abstractive opinion summarization.
Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models
Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English.
mFACE: Multilingual Summarization with Factual Consistency Evaluation
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets.
QAmeleon: Multilingual QA with Only 5 Examples
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA).
Conditional Generation with a Question-Answering Blueprint
The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Common Sense Reasoning
on BIG-bench (Known Unknowns)
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies.
Shatter: An Efficient Transformer Encoder with Single-Headed Self-Attention and Relative Sequence Partitioning
The highly popular Transformer architecture, based on self-attention, is the foundation of large pretrained models such as BERT, that have become an enduring paradigm in NLP.
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Professional summaries are written with document-level information, such as the theme of the document, in mind.
Planning with Learned Entity Prompts for Abstractive Summarization
Moreover, we demonstrate empirically that planning with entity chains provides a mechanism to control hallucinations in abstractive summaries.
Stepwise Extractive Summarization and Planning with Structured Transformers
We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers.
On Faithfulness and Factuality in Abstractive Summarization
It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation.
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations.
Ranked #2 on
Part-Of-Speech Tagging
on Penn Treebank
