Papers Extractive Text Summarization

Ranking Sentences for Extractive Summarization with Reinforcement Learning

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking tas…

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting…

Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving …

Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries…

Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries…

Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks

Due to the large amount of textual information available on Internet, it is of paramount relevance to use techniques that find relevant and concise content. A typical task devoted to the identification of informative sen…

Get To The Point: Summarization with Pointer-Generator Networks

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, th…

Centroid-based Text Summarization through Compositionality of Word Embeddings

The textual similarity is a crucial aspect for many extractive text summarization methods. A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related…

A New Persian Text Summarization Approach Based on Natural Language Processing and Graph Similarity

Abstract: A significant amount of available information is stored in textual databases which contain a large collection of documents from different sources (such as news, articles, books, emails and web pages). The in…

Classify or Select: Neural Architectures for Extractive Document Summarization

We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the or…

A Neural Attention Model for Abstractive Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summa…

A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization

Multi-layered graph-based multi-document summarization model

Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by t…

Using Supervised Bigram-based ILP for Extractive Summarization

Using Document Summarization Techniques for Speech Data Subset Selection

Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality