Papers Extractive Text Summarization

Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such…

Leveraging BERT for Extractive Text Summarization on Lectures

In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches…

A Novel System for Extractive Clinical Note Summarization using EHR Data

While much data within a patient{'}s electronic health record (EHR) is coded, crucial information concerning the patient{'}s care and management remain buried in unstructured clinical notes, making it difficult and time-…

HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization

Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the …

Fine-tune BERT for Extractive Summarization

BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state o…

Neural Extractive Text Summarization with Syntactic Compression

Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint ex…

Language Model Pre-training for Hierarchical Document Representations

Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis. However, eff…

Efficient Attention: Attention with Linear Complexities

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on…

BoWLer: A neural approach to extractive text summarization

DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-b…

Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN

Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the stren…

Iterative Document Representation Learning Towards Summarization with Polishing

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article…

BanditSum: Extractive Summarization as a Contextual Bandit

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extr…

Neural Latent Extractive Document Summarization

Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these …

Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks

Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel …

Neural Document Summarization by Jointly Learning to Score and Select Sentences

Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural n…

Document Modeling with External Attention for Sentence Extraction

Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop …

A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)

The recent advance in neural network architecture and training algorithms have shown the effectiveness of representation learning. The neural network-based models generate better representation than the traditional ones.…

Using Statistical and Semantic Models for Multi-Document Summarization

We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better capture word co-occurrences and distribution a…

Extractive Text Summarization using Neural Networks

Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward…