Sentence Fusion

12 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

Greatest papers with code

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.

Language understanding Machine Translation +5

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.

Automatic Post-Editing Language Modelling +3

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.

Grammatical Error Correction Sentence Fusion +1

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.

Abstractive Text Summarization Document Summarization +2

DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion

google-research-datasets/discofuse NAACL 2019

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.

Sentence Fusion Text Simplification +1

Understanding Points of Correspondence between Sentences for Abstractive Summarization

ucfnlp/points-of-correspondence ACL 2020

We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.

Abstractive Text Summarization Coreference Resolution +1

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.

Sentence Fusion

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

jiacheng-xu/sum-interpret ACL 2021

Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.

Abstractive Text Summarization Language Modelling +1

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.

Data-to-Text Generation Domain Adaptation +2