Абстрактное Резюмирование

AbstractIn this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting. We design a paraphrastic sentence fusion model which jointly performs sentence fusion and paraphrasing …...

AbstractWe address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the …...

AbstractDeep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward Rouge-L simply …...

AbstractThis paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks. Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI …...

AbstractWe propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task. Target texts are reconstructed from the inputs using three main edit operations: keeping …...

AbstractEntities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is …...

AbstractAbstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which …...

AbstractAbstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a …...

AbstractGiven a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in …...

AbstractRecent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch …...

AbstractTraditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the …...

AbstractSocial media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true …...

AbstractSummarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform …...

ive Summaries Tim Fischer Steffen Remus Chris Biemann Language Technology Group Department of Informatics Universität Hamburg, Germany {firstname.lastname}@uni-hamburg.de Abstract Recent abstractive summarization systems fail to generate factually consistent – faithful …...

AbstractAlthough the fluency of automatically generated abstractive summaries has improved significantly with advanced methods, the inconsistency that remains in summarization is recognized as an issue to be addressed. In this …...

AbstractThe topic of summarization evaluation has recently attracted a surge of attention due to the rapid development of abstractive summarization systems. However, the formulation of the task is rather ambiguous, …...

Abstract“Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a …...

AbstractIn this work, we study the importance of content frequency on abstractive summarization, where we define the content as “semantic units.” We propose a two-stage training framework to let the …...