Papers Sentence Ordering
“Sentence Ordering” 태그가 달린 논문 51편 · 필터 해제
ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts
Summarizing deeply nested discussion threads requires handling interleaved replies, quotes, and overlapping topics, which standard LLM summarizers struggle to capture reliably. We introduce ThreadSumm, a multi-stage LLM …
Sentence OrderingBurn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?
This paper introduces the TempVS benchmark, which focuses on temporal grounding and reasoning capabilities of Multimodal Large Language Models (MLLMs) in image sequences. TempVS consists of three main tests (i.e., event …
SentenceSentence OrderingOrderSum: Semantic Sentence Ordering for Extractive Summarization
There are two main approaches to recent extractive summarization: the sentence-level framework, which selects sentences to include in a summary individually, and the summary-level framework, which generates multiple cand…
Extractive SummarizationSentenceSentence OrderingEconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms of economics, business, and supply chain …
ArticlesManagementMultiple Choice Question Answering (MCQA)+2Are Large Language Models Temporally Grounded?
Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we provide LLMs with textual narratives and …
Common Sense ReasoningIn-Context LearningSentenceSentence OrderingNon-Autoregressive Sentence Ordering
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step. Such an autoregressive manner only lev…
DecoderSentenceSentence OrderingReading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs), in which named entity recognition (NER) is treated as a sequence-labeling task of p…
Entity LinkingKey Information ExtractionKey-value Pair Extractionnamed-entity-recognition+9Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
Positional reasoning is the process of ordering unsorted parts contained in a set into a consistent structure. We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to add…
Graph Neural NetworkSentenceSentence OrderingVisual StorytellingDiscourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLM…
SentenceSentence OrderingSet Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction
The task of learning to map an input set onto a permuted sequence of its elements is challenging for neural networks. Set-to-sequence problems occur in natural language processing, computer vision and structure predictio…
Combinatorial OptimizationRelational ReasoningSentenceSentence OrderingPruned Graph Neural Network for Short Story Ordering
Text coherence is a fundamental problem in natural language generation and understanding. Organizing sentences into an order that maximizes coherence is known as sentence ordering. This paper is proposing a new approach …
Graph Neural NetworkSentenceSentence OrderingText GenerationPhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics
In order for language models to aid physics research, they must first encode representations of mathematical and natural language discourse which lead to coherent explanations, with correct ordering and relevance of stat…
PositionSentenceSentence OrderingAssessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models
The task of ordering a shuffled set of sentences into a coherent text is used to evaluate the capacity of a model to understand causal and temporal relations between entities and events. Recent approaches rely on pretrai…
DiversitySentenceSentence OrderingA Neural Graph-based Local Coherence Model
Entity grids and entity graphs are two frameworks for modeling local coherence. These frameworks represent entity relations between sentences and then extract features from such representations to encode coherence. The b…
Coherence EvaluationmodelSentenceSentence OrderingImproving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementar…
SentenceSentence OrderingLocal and Global Context-Based Pairwise Models for Sentence Ordering
Sentence Ordering refers to the task of rearranging a set of sentences into the appropriate coherent order. For this task, most previous approaches have explored global context-based end-to-end methods using Sequence Gen…
SentenceSentence OrderingSTaCK: Sentence Ordering with Temporal Commonsense Knowledge
Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological se…
Edge ClassificationSentenceSentence OrderingA New Sentence Ordering Method Using BERT Pretrained Model
Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of se…
Natural Language UnderstandingSentenceSentence EmbeddingSentence-Embedding+2Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering
Discovering the logical sequence of events is one of the cornerstones in Natural Language Understanding. One approach to learn the sequence of events is to study the order of sentences in a coherent text. Sentence orderi…
Document SummarizationLanguage ModelingLanguage ModellingLogical Sequence+7Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman Problem
The task of Sentence Ordering refers to rearranging a set of given sentences in a coherent ordering. Prior work (Prabhumoye et al., 2020) models this as an optimal graph traversal (with sentences as nodes, and edges as l…
Combinatorial OptimizationSentenceSentence OrderingTraveling Salesman Problem