Sentence Fusion
19 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
Latest papers with no code
EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start
This is achieved by decomposing the generation process into three sub-tasks: (1) tagging to decide on the subset of input tokens to be preserved in the output, (2) re-ordering to define their order in the output text, and (3) insertion to infill the missing tokens that are not present in the input.
Large-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents.
Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
NLP models that process multiple texts often struggle in recognizing corresponding and salient information that is often differently phrased, and consolidating the redundancies across texts.
Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics
Our experimental results on three real-world datasets show that both the extractive and abstractive approaches outperform four strong baselines in terms of facilitating topic understanding and interpretation.
Unsupervised Text Style Transfer with Padded Masked Language Models
This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain.
Analyzing Sentence Fusion in Abstractive Summarization
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.
Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions
The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers.
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion
Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences.
Deep Learning Approaches to Text Production
Each text production task raises a slightly different communication goal (e. g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation).
Abstractive Meeting Summarization UsingDependency Graph Fusion
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries.