Papers Sentence Fusion
“Sentence Fusion” 태그가 달린 논문 36편 · 필터 해제
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, tradit…
Natural Language InferenceSentenceSentence FusionNon-autoregressive Text Editing with Copy-aware Latent Alignments
Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2E…
ManagementSentenceSentence FusionRedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans
The text editing tasks, including sentence fusion, sentence splitting and rephrasing, text simplification, and Grammatical Error Correction (GEC), share a common trait of dealing with highly similar input and output sequ…
Grammatical Error CorrectionMachine Translationnamed-entity-recognitionNamed Entity Recognition+7Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations
Traditional sentence embedding models encode sentences into vector representations to capture useful properties such as the semantic similarity between sentences. However, in addition to similarity, sentence semantics ca…
DecoderRepresentation LearningSemantic SimilaritySemantic Textual Similarity+5CoEdIT: Text Editing by Task-Specific Instruction Tuning
We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it …
Formality Style TransferGrammatical Error CorrectionLanguage ModelingLarge Language Model+4Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document. Most recent research…
Grammatical Error CorrectionSentenceSentence FusionStyle Transfer+1ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substa…
Data-to-Text GenerationSentenceSentence FusionText GenerationPhrase-Level Localization of Inconsistency Errors in Summarization by Weak Supervision
Although 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 s…
ARCSentenceSentence FusionSummarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization P…
Abstractive Text SummarizationSentenceSentence FusionEdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start
We present EdiT5 - a novel semi-autoregressive text-editing model designed to combine the strengths of non-autoregressive text-editing and autoregressive decoding. EdiT5 is faster during inference than conventional seque…
DecoderGrammatical Error CorrectionSentenceSentence FusionLarge-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling differen…
Abstractive Text SummarizationDocument SummarizationLanguage ModelingLanguage Modelling+3Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
NLP models that process multiple texts often struggle in recognizing corresponding and salient information that is often differently phrased, and consolidating the redundancies across texts. To facilitate research of suc…
SentenceSentence FusionEvent Graph based Sentence Fusion
Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has long been recognized by communi…
Abstractive Text SummarizationSentenceSentence FusionText Generation+1Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
NLP models that compare or consolidate information across multiple documents often struggle when challenged with recognizing substantial information redundancies across the texts. For example, in multi-document summariza…
Document SummarizationMulti-Document SummarizationSentenceSentence FusionExtractive and Abstractive Sentence Labelling of Sentiment-bearing Topics
This paper tackles the problem of automatically labelling sentiment-bearing topics with descriptive sentence labels. We propose two approaches to the problem, one extractive and the other abstractive. Both approaches rel…
DescriptiveSentenceSentence FusionDissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We propose a two-step method to interpret su…
Abstractive Text SummarizationDecoderLanguage ModelingLanguage Modelling+3Improving Human Text Simplification with Sentence Fusion
The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of huma…
RerankingSentenceSentence FusionText SimplificationData-to-Text Generation with Iterative Text Editing
We present a novel approach to data-to-text generation based on iterative text editing. Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-traine…
Data-to-Text GenerationDomain AdaptationLanguage ModelingLanguage Modelling+3Learning to Fuse Sentences with Transformers for Summarization
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce…
SentenceSentence FusionSemantically Driven Sentence Fusion: Modeling and Evaluation
Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants.…
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