@inproceedings{pranjic-etal-2026-fusing,
title = "Fusing Sentence-Level and Document-Level Representations for Keyphrase Extraction",
author = "Pranji{\'c}, Marko and
Armeni, Kristijan and
Pollak, Senja",
editor = "Accomazzi, Alberto and
Ghosal, Tirthankar and
Grezes, Felix and
Lockhart, Kelly and
Alkan, Atilla Kaan",
booktitle = "Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wasp-1.14/",
pages = "122--132",
ISBN = "979-8-89176-456-9",
abstract = "Supervised sequence-labeling approaches to keyphrase extraction operate at token level, but whether a phrase is relevant may depend on context beyond its sentence. Processing an entire document at token resolution can increase sequence length and introduce irrelevant information. We investigate a mixed-resolution context-fusion architecture that retains token representations for the target sentence while compressing available surrounding sentences into pooled representations. Both representation types are placed in document order and processed jointly by a self-attention module before IOB prediction. We evaluate the model on $\textsf{kp20k}$, $\textsf{inspec}$, and $\textsf{kptimes}$ against SEKE, a recent state-of-the-art supervised system with approximately 60{\%} more total parameters. The model achieves comparable F1 scores on the two scientific-domain benchmarks, while a larger performance gap remains on the journalistic benchmark. Error analysis shows that the model generates substantially more candidates than there are gold keyphrases. However, many predictions counted as false positives under exact-match evaluation partially overlap with gold keyphrases, while exact matches tend to receive higher scores than partial and non-matches."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pranjic-etal-2026-fusing">
<titleInfo>
<title>Fusing Sentence-Level and Document-Level Representations for Keyphrase Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marko</namePart>
<namePart type="family">Pranjić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kristijan</namePart>
<namePart type="family">Armeni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Senja</namePart>
<namePart type="family">Pollak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alberto</namePart>
<namePart type="family">Accomazzi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tirthankar</namePart>
<namePart type="family">Ghosal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Felix</namePart>
<namePart type="family">Grezes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kelly</namePart>
<namePart type="family">Lockhart</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Atilla</namePart>
<namePart type="given">Kaan</namePart>
<namePart type="family">Alkan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hengqin, Zhuhai, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-456-9</identifier>
</relatedItem>
<abstract>Supervised sequence-labeling approaches to keyphrase extraction operate at token level, but whether a phrase is relevant may depend on context beyond its sentence. Processing an entire document at token resolution can increase sequence length and introduce irrelevant information. We investigate a mixed-resolution context-fusion architecture that retains token representations for the target sentence while compressing available surrounding sentences into pooled representations. Both representation types are placed in document order and processed jointly by a self-attention module before IOB prediction. We evaluate the model on kp20k, inspec, and kptimes against SEKE, a recent state-of-the-art supervised system with approximately 60% more total parameters. The model achieves comparable F1 scores on the two scientific-domain benchmarks, while a larger performance gap remains on the journalistic benchmark. Error analysis shows that the model generates substantially more candidates than there are gold keyphrases. However, many predictions counted as false positives under exact-match evaluation partially overlap with gold keyphrases, while exact matches tend to receive higher scores than partial and non-matches.</abstract>
<identifier type="citekey">pranjic-etal-2026-fusing</identifier>
<location>
<url>https://aclanthology.org/2026.wasp-1.14/</url>
</location>
<part>
<date>2026-11</date>
<extent unit="page">
<start>122</start>
<end>132</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Fusing Sentence-Level and Document-Level Representations for Keyphrase Extraction
%A Pranjić, Marko
%A Armeni, Kristijan
%A Pollak, Senja
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%Y Alkan, Atilla Kaan
%S Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-456-9
%F pranjic-etal-2026-fusing
%X Supervised sequence-labeling approaches to keyphrase extraction operate at token level, but whether a phrase is relevant may depend on context beyond its sentence. Processing an entire document at token resolution can increase sequence length and introduce irrelevant information. We investigate a mixed-resolution context-fusion architecture that retains token representations for the target sentence while compressing available surrounding sentences into pooled representations. Both representation types are placed in document order and processed jointly by a self-attention module before IOB prediction. We evaluate the model on kp20k, inspec, and kptimes against SEKE, a recent state-of-the-art supervised system with approximately 60% more total parameters. The model achieves comparable F1 scores on the two scientific-domain benchmarks, while a larger performance gap remains on the journalistic benchmark. Error analysis shows that the model generates substantially more candidates than there are gold keyphrases. However, many predictions counted as false positives under exact-match evaluation partially overlap with gold keyphrases, while exact matches tend to receive higher scores than partial and non-matches.
%U https://aclanthology.org/2026.wasp-1.14/
%P 122-132
Markdown (Informal)
[Fusing Sentence-Level and Document-Level Representations for Keyphrase Extraction](https://aclanthology.org/2026.wasp-1.14/) (Pranjić et al., WASP 2026)
ACL