Papers with extractive summarization methods

5 papers
Comprehensive Abstractive Comment Summarization with Dynamic Clustering and Chain of Thought (2024.findings-acl)

Copied to clipboard

Challenge: Recent work on news comment summarization has focused on extractive methods within constraints.
Approach: They propose an enhanced fast clustering algorithm that maintains a dynamic similarity threshold to ensure high density of each comment cluster being built.
Outcome: The proposed method improves the baseline methods and the test suite on real-world news comments.
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)

Copied to clipboard

Challenge: lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations.
Approach: They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries.
Outcome: The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis.
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)

Copied to clipboard

Challenge: Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare .
Approach: They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods .
Outcome: The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users.
TSix: A Human-involved-creation Dataset for Tweet Summarization (L18-1)

Copied to clipboard

Challenge: a new dataset for tweet summarization is available for free.
Approach: They propose a dataset for tweet summarization that uses human annotations to evaluate extractive summarizing methods.
Outcome: The proposed dataset includes six events collected from Twitter . human-annotated gold-standard references facilitate evaluation, the study shows .
Annotation and Analysis of Extractive Summaries for the Kyutech Corpus (L18-1)

Copied to clipboard

Challenge: Summarization of multi-party conversation requires corpora to analyze characteristics of conversations and construct a method for summary generation.
Approach: They propose to annotate a Japanese conversation corpus for a decision-making task . they compare extractive summarization methods with the annotated extractive summary .
Outcome: The proposed corpus is the first annotated for conversation summarization tasks and freely available to anyone.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations