Multi-Document Summarization

69 papers with code • 4 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

Most implemented papers

Bottom-Up Abstractive Summarization

sebastianGehrmann/bottom-up-summary EMNLP 2018

We use this selector as a bottom-up attention step to constrain the model to likely phrases.

Generating Wikipedia by Summarizing Long Sequences

tensorflow/tensor2tensor ICLR 2018

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

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.

Centroid-based Text Summarization through Compositionality of Word Embeddings

gaetangate/text-summarizer WS 2017

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

Alex-Fabbri/Multi-News ACL 2019

Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.

Global-aware Beam Search for Neural Abstractive Summarization

yema2018/global_aware NeurIPS 2021

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

ibm/kpa_2021_shared_task EMNLP 2020

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.