Extractive Text Summarization
32 papers with code • 5 benchmarks • 5 datasets
Given a document, selecting a subset of the words or sentences which best represents a summary of the document.
Benchmarks
These leaderboards are used to track progress in Extractive Text Summarization
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Libraries
Use these libraries to find Extractive Text Summarization models and implementations
CNN/Daily Mail
GovReport
DUC 2004
DebateSum
Latest papers with no code
RankSum 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, semantic content, significant keywords, and position.
Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization
While SSL is popular for image and text classification, it is relatively underexplored for the task of extractive text summarization.
CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19
To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset.
San-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) using Devanagari Sanskrit text corpus.
Efficient 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.
Extractive 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 models and architectures that, despite producing notable results, usually turn out in models difficult to interpret.
Neural 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.
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Using various experimental settings on three datasets (i. e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected.
Extractive 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.
GUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features
In this way, we define the edges of a graph where semantic similarities are represented.
