reordering keyphrases with apparition order in text

README.md

@@ -35,7 +35,7 @@ This version of the dataset was produced by [(Boudin et al., 2016)][boudin-2016]
   * `lvl-2`: for each file, we manually retrieved the original PDF file from the ACM Digital Library.
     We then extract the enriched textual content of the PDF files using an Optical Character Recognition (OCR) system and perform document logical structure detection using ParsCit v110505.
     We use the detected logical structure to remove author-assigned keyphrases and select only relevant elements : title, headers, abstract, introduction, related work, body text and conclusion.
-    We finally apply a systematic dehyphenation at line breaks.
+    We finally apply a systematic dehyphenation at line breaks.s
 
   * `lvl-3`: we further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.
 
@@ -50,6 +50,7 @@ They are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</
 Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
 Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
 Details about the process can be found in `prmu.py`.
+The <u>P</u>resent reference keyphrases are also ordered by their order of apparition in the concatenation of title and text (lvl-1).
 
 ## Content and statistics
 
@@ -92,4 +93,4 @@ The following data fields are available :
 [kim-2010]: https://aclanthology.org/S10-1004/
 [chaimongkol-2014]: https://aclanthology.org/L14-1259/
 [boudin-2016]: https://aclanthology.org/W16-3917/
-[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
+[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/

test.jsonl

@@ -1,3 +1,3 @@
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-size 11239360
+oid sha256:615ebea82047b901dbb0f6bda32278c41ed99f7a8d824ef8e371e234229fc52c
+size 11238079

train.jsonl

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+oid sha256:c72af40191d12c41e410200eadf28756f36a791c4345771dd2d9362b8241c4c4
+size 16055534