Sentence Fusion
18 papers with code • 1 benchmarks • 3 datasets
Sentence Fusion is the task of combining several independent sentences into a single coherent text. Sentence Fusion is important in many NLP applications, including retrieval-based dialogue, text summarization and question answering.
Source: DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
Benchmarks
These leaderboards are used to track progress in Sentence Fusion
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
WikiSplit
DiscoFuse
Most implemented papers
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
huggingface/transformers
•
•
TACL 2020
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
Encode, Tag, Realize: High-Precision Text Editing
google-research/lasertagger
•
•
IJCNLP 2019
We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
ucfnlp/summarization-sing-pair-mix
•
•
ACL 2019
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.
Felix: Flexible Text Editing Through Tagging and Insertion
google-research/google-research
•
•
Findings of the Association for Computational Linguistics 2020
We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.
Understanding Points of Correspondence between Sentences for Abstractive Summarization
We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.
Seq2Edits: Sequence Transduction Using Span-level Edit Operations
tensorflow/tensor2tensor
•
•
EMNLP 2020
For text normalization, sentence fusion, and grammatical error correction, our approach improves explainability by associating each edit operation with a human-readable tag.
Semantically Driven Sentence Fusion: Modeling and Evaluation
eyalbd2/Semantically-Driven-Sentence-Fusion
•
•
Findings of the Association for Computational Linguistics 2020
Sentence fusion is the task of joining related sentences into coherent text.
Learning to Fuse Sentences with Transformers for Summarization
ucfnlp/sent-fusion-transformers
•
•
EMNLP 2020
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.
Data-to-Text Generation with Iterative Text Editing
kasnerz/d2t_iterative_editing
•
•
INLG (ACL) 2020
Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LaserTagger) and language modeling (GPT-2) to improve the text fluency.
