Sentence Compression
22 papers with code • 1 benchmarks • 2 datasets
Sentence Compression is the task of reducing the length of text by removing non-essential content while preserving important facts and grammaticality.
Benchmarks
These leaderboards are used to track progress in Sentence Compression
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Sentence Compression
Google
Latest papers with no code
Analyse de la r\'egulation de la longueur dans un syst\`eme neuronal de compression de phrase : une \'etude du mod\`ele LenInit (Investigating Length Regulation in a Sentence Compression Neural System : a Study on the LenInit Model)
Dans la continuit{\'e} de la litt{\'e}rature d{\'e}di{\'e}e {\`a} la compr{\'e}hension du comportement des syst{\`e}mes neuronaux, nous examinons dans cet article les m{\'e}canismes de r{\'e}gulation de longueur d{'}un encodeur-d{\'e}codeur RNN appliqu{\'e} {\`a} la compression de phrase, en {\'e}tudiant sp{\'e}cifiquement le cas du mod{\`e}le LenInit.
Combining Word Embeddings and N-grams for Unsupervised Document Summarization
An improved sentence similarity graph is built and used in a submodular objective function for extractive summarization, which consists of a weighted coverage term and a diversity term.
A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming
Multi-Sentence Compression (MSC) aims to generate a short sentence with the key information from a cluster of similar sentences.
Machine Translation with Unsupervised Length-Constraints
In this paper, we explore one of these, the generation of constraint translation.
A Difference-of-Convex Programming Approach With Parallel Branch-and-Bound For Sentence Compression Via A Hybrid Extractive Model
In this paper, we design a hybrid extractive sentence compression model combining a probability language model and a parse tree language model for compressing sentences by guaranteeing the syntax correctness of the compression results.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level
Highlighting is a powerful tool to pick out important content and emphasize.
Cross-Task Knowledge Transfer for Query-Based Text Summarization
We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization.
Query-focused Sentence Compression in Linear Time
Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface.
Towards Annotating and Creating Sub-Sentence Summary Highlights
Highlighting is a powerful tool to pick out important content and emphasize.
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
In this work, we propose a fully unsupervised model, Deleter, that is able to discover an "optimal deletion path" for an arbitrary sentence, where each intermediate sequence along the path is a coherent subsequence of the previous one.
