Papers Extractive Text Summarization

HiStruct+: 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…

Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer

Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-traine…

Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization

Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semanti…

A Hybrid PSO-GA for Extractive Text Summarization

Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization

Recent researches have demonstrated that BERT shows potential in a wide range of natural language processing tasks. It is adopted as an encoder for many state-of-the-art automatic summarizing systems, which achieve excel…

Multiplex Graph Neural Network for Extractive Text Summarization

Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Re…

Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization

“Extractive text summarization seeks to extract indicative sentences from a source document andassemble them to form a summary. Selecting salient but not redundant sentences has alwaysbeen the main challenge. Unlike the …

MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes

We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When…

Topic Modeling Based Extractive Text Summarization

Text summarization is an approach for identifying important information present within text documents. This computational technique aims to generate shorter versions of the source text, by including only the relevant and…

CX DB8: A queryable extractive summarizer and semantic search engine

Competitive Debate's increasingly technical nature has left competitors looking for tools to accelerate evidence production. We find that the unique type of extractive summarization performed by competitive debaters - su…

Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples

In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. In this paper, we will discuss novel methods to generate an extractive summary by scoring the …

DebateSum: A large-scale argument mining and summarization dataset

Prior work in Argument Mining frequently alludes to its potential applications in automatic debating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to p…

Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network

Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between …

Improving Neural Text Summarization using Knowledge Graphs

In this paper, we propose a method for extractive text summarization using auto-regressive transformers. For better learning procedure we adopt the knowledge graph method to convert our textual data to more informative t…

Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers

Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge …

Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, m…

MultiGBS: A multi-layer graph approach to biomedical summarization

Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of te…

Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches

The authors present a work-in-progress in the field of Abstractive Text Summarization (ATS) for Sanskrit Prose {--} a first attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS …

Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization

The intrinsic and extrinsic quality evaluation is an essential part of the summary evaluation methodology usually conducted in a traditional controlled laboratory environment. However, processing large text corpora using…

Screenplay Summarization Using Latent Narrative Structure

Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart …