Papers Unsupervised Extractive Summarization

Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space

We present HyperSum, an extractive summarization framework that captures both the efficiency of traditional lexical summarization and the accuracy of contemporary neural approaches. HyperSum exploits the pseudo-orthogona…

Unsupervised Extractive Summarization with Learnable Length Control Strategies

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus ca…

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, c…

Unsupervised Extractive Summarization of Emotion Triggers

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained s…

XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages

Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only …

Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization

Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data. Recent literatures mostly research how to leverage sentence similarity to rank sentences in the…

Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document

In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statisti…

Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions

Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document s…

Unsupervised Text Summarization of Long Documents using Dependency-based Noun Phrases and Contextual Order Arrangement

Unsupervised extractive summarization has recently gained importance since it does not require labeled data. Among unsupervised methods, graph-based approaches have achieved outstanding results. These methods represent e…

Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm

We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial probl…

On the Trade-off between Redundancy and Local Coherence in Summarization

Extractive summaries are usually presented as lists of sentences with no expected cohesion between them and with plenty of redundant information if not accounted for. In this paper, we investigate the trade-offs incurred…

A 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…

TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are ask…

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are ask…

Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering

We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations,…

Improving Unsupervised Extractive Summarization with Facet-Aware Modeling

Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has be…

Unsupervised Extractive Summarization by Human Memory Simulation

Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured doc…

Unsupervised Extractive Summarization using Pointwise Mutual Information

Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document. We propose new metrics of relevance and redundan…

Unsupervised Abstractive Summarization of Bengali Text Documents

Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of parallel data for low-reso…