88 papers with code • 5 benchmarks • 14 datasets
Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.
Source: Multi-Document Summarization using Distributed Bag-of-Words Model
These leaderboards are used to track progress in Multi-Document Summarization
Most implemented papers
Bottom-Up Abstractive Summarization
We use this selector as a bottom-up attention step to constrain the model to likely phrases.
Generating Wikipedia by Summarizing Long Sequences
We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Centroid-based Text Summarization through Compositionality of Word Embeddings
The textual similarity is a crucial aspect for many extractive text summarization methods.
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries.
Pre-training via Paraphrasing
The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks.
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual.
Global-aware Beam Search for Neural Abstractive Summarization
A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion.
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments.