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Found 50 papers, 16 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).
Learning to Plan and Generate Text with Citations
The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Calibrating Likelihoods towards Consistency in Summarization Models
Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application.
$μ$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.
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.
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.
Query Refinement Prompts for Closed-Book Long-Form Question Answering
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings.
Calibrating Sequence likelihood Improves Conditional Language Generation
Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences.
Ranked #1 on
Abstractive Text Summarization
on CNN / Daily Mail
abstractive question answering
Abstractive Text Summarization
+5
SMART: Sentences as Basic Units for Text Evaluation
Specifically, We treat sentences as basic units of matching instead of tokens, and use a sentence matching function to soft-match candidate and reference sentences.
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.
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.
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
We show via data analysis that it's not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles.
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.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
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.
QURIOUS: Question Generation Pretraining for Text Generation
Recent trends in natural language processing using pretraining have shifted focus towards pretraining and fine-tuning approaches for text generation.
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
We address the issue of hallucination in data-to-text generation, i. e., reducing the generation of text that is unsupported by the source.
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
Ranked #1 on
Split and Rephrase
on WikiSplit
What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks
We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the article about?''.
HighRES: Highlight-based Reference-less Evaluation of Summarization
There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches.
Jointly Extracting and Compressing Documents with Summary State Representations
We present a new neural model for text summarization that first extracts sentences from a document and then compresses them.
Privacy-preserving Neural Representations of Text
This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection.
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach.
Ranked #9 on
Text Summarization
on X-Sum
Local String Transduction as Sequence Labeling
We show that the general problem of string transduction can be reduced to the problem of sequence labeling.
Document Modeling with External Attention for Sentence Extraction
Document modeling is essential to a variety of natural language understanding tasks.
Deep Learning Approaches to Text Production
Each text production task raises a slightly different communication goal (e. g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation).
Ranking Sentences for Extractive Summarization with Reinforcement Learning
In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
Ranked #13 on
Extractive Text Summarization
on CNN / Daily Mail
The WebNLG Challenge: Generating Text from RDF Data
The WebNLG challenge consists in mapping sets of RDF triples to text.
Split and Rephrase
We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences.
Creating Training Corpora for NLG Micro-Planners
In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases.
Neural Extractive Summarization with Side Information
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Optimizing Spectral Learning for Parsing
We describe a search algorithm for optimizing the number of latent states when estimating latent-variable PCFGs with spectral methods.
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer.
Encoding Prior Knowledge with Eigenword Embeddings
Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views.
Unsupervised Sentence Simplification Using Deep Semantics
We present a novel approach to sentence simplification which departs from previous work in two main ways.
Ranked #2 on
Text Simplification
on PWKP / WikiSmall
Diversity in Spectral Learning for Natural Language Parsing
We describe an approach to create a diverse set of predictions with spectral learning of latent-variable PCFGs (L-PCFGs).
