Sentence Summarization
19 papers with code • 0 benchmarks • 0 datasets
Generating a summary of a given sentence.
Benchmarks
These leaderboards are used to track progress in Sentence Summarization
Latest papers with no code
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence.
Unsupervised Text Summarization via Mixed Model Back-Translation
Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer.
Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction.
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Multi-Source Pointer Network for Product Title Summarization
For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence.
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques.
A multi-task learning model for malware classification with useful file access pattern from API call sequence
Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification.
Online Segment to Segment Neural Transduction
We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read.
