Search Results for author: Dan Lahav

Found 11 papers, 4 papers with code

A Search Engine for Discovery of Scientific Challenges and Directions

1 code implementation NeurIPS Workshop AI4Scien 2021 Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope

To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.

Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis

2 code implementations EMNLP 2020 Roy Bar-Haim, Yoav Kantor, Lilach Eden, Roni Friedman, Dan Lahav, Noam Slonim

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.

Document Summarization Key Point Matching +1

Interactive Extractive Search over Biomedical Corpora

no code implementations WS 2020 Hillel Taub-Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen, Yoav Goldberg

We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries.

Retrieval Sentence

From Arguments to Key Points: Towards Automatic Argument Summarization

no code implementations ACL 2020 Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem.

A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis

2 code implementations26 Nov 2019 Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim

To this end, we created a corpus of 30, 497 arguments carefully annotated for point-wise quality, released as part of this work.

Automatic Argument Quality Assessment -- New Datasets and Methods

no code implementations3 Sep 2019 Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim

In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.

Language Modelling

Argument Invention from First Principles

no code implementations ACL 2019 Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim

In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.

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