Papers Extractive Text Summarization
“Extractive Text Summarization” 태그가 달린 논문 96편 · 필터 해제
Efficient Extractive Text Summarization for Online News Articles Using Machine Learning
In the age of information overload, content management for online news articles relies on efficient summarization to enhance accessibility and user engagement. This article addresses the challenge of extractive text summ…
Extractive Text SummarizationBinary ClassificationA Novel Word Pair-based Gaussian Sentence Similarity Algorithm For Bengali Extractive Text Summarization
Extractive Text Summarization is the process of selecting the most representative parts of a larger text without losing any key information. Recent attempts at extractive text summarization in Bengali, either relied on s…
ArticlesExtractive SummarizationExtractive Text SummarizationSentence+2Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization
In an era where digital text is proliferating at an unprecedented rate, efficient summarization tools are becoming indispensable. While Large Language Models (LLMs) have been successfully applied in various NLP tasks, th…
Extractive SummarizationExtractive Text SummarizationLanguage ModelingLanguage Modelling+3A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference time to generate summaries directly; however, such approaches often suffer from incon…
Abstractive Text SummarizationData AugmentationExtractive SummarizationExtractive Text Summarization+5RankSum An unsupervised extractive text summarization based on rank fusion
In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, se…
Extractive Text SummarizationSentenceSentence EmbeddingsText Summarization+1Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization
Semi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. While SSL is popular for image and text classification, it is relatively underexplored f…
Extractive SummarizationExtractive Text Summarizationtext-classificationText Classification+1Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization Model
In this era where a large amount of information has flooded the Internet, manual extraction and consumption of relevant information is very difficult and time-consuming. Therefore, an automated document summarization too…
ClusteringDocument Summarizationdocument understandingExtractive Text Summarization+4CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19
The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. I…
ArticlesDocument SummarizationExtractive Text SummarizationText Summarization+1San-BERT: Extractive Summarization for Sanskrit Documents using BERT and it's variants
In this work, we develop language models for the Sanskrit language, namely Bidirectional Encoder Representations from Transformers (BERT) and its variants: A Lite BERT (ALBERT), and Robustly Optimized BERT (RoBERTa) usin…
ClusteringExtractive SummarizationExtractive Text SummarizationText SummarizationAlign and Attend: Multimodal Summarization with Dual Contrastive Losses
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimodal summarization task explicitly levera…
Extractive Text SummarizationSupervised Video SummarizationVideo SummarizationEfficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods require a natural differentiable extension of…
Efficient ExplorationExtractive Text SummarizationImage RetrievalRetrieval+1Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense mode…
Additive modelsBinary ClassificationExtractive SummarizationExtractive Text Summarization+2Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)
It is well recognized that creating summaries of scientific texts can be difficult. For each given document, the majority of summarizing research believes there is only one best gold summary. Having just one gold summary…
Document SummarizationExtractive Text SummarizationGraph AttentionScientific Document Summarization+2GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calcul…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationGraph Ranking+5IDN-Sum: A New Dataset for Interactive Digital Narrative Extractive Text Summarisation
Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper,…
Extractive Text SummarizationText SummarizationNeural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and docum…
Extractive SummarizationExtractive Text SummarizationSentenceText SummarizationHiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy.…
Extractive SummarizationExtractive Text SummarizationLanguage ModelingLanguage Modelling+2Extractive Text Summarization with Latent Topics using Heterogeneous Graph Neural Network
This paper presents a heterogeneous graph neural network (HeterGNN) model for extractive text summarization (ETS) by using latent topics to capture the important content of input documents. Specifically, topical informat…
Extractive Text SummarizationGraph Neural NetworkSentenceText SummarizationGUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features
Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are mostly created by calcula…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationGraph Ranking+5A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods
In recent years, text summarization methods have attracted much attention again thanks to the researches on neural network models. Most of the current text summarization methods based on neural network models are supervi…
Extractive SummarizationExtractive Text SummarizationSentenceText Summarization+1