Search Results for author: Demian Gholipour Ghalandari

Found 5 papers, 4 papers with code

A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

1 code implementation ACL 2020 Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham, John Glover, Georgiana Ifrim

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.

Clustering Document Summarization +1

Examining the State-of-the-Art in News Timeline Summarization

1 code implementation ACL 2020 Demian Gholipour Ghalandari, Georgiana Ifrim

Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.

Timeline Summarization

Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization

no code implementations WS 2017 Demian Gholipour Ghalandari

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector.

Document Summarization Extractive Document Summarization +2

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