Long document summarization, a challenging summarization scenario, is the focus of the recently proposed LongSumm shared task. One of the limitations of this shared task has been its use of a single family of metrics for evaluation (the ROUGE metrics). In contrast, other fields, like text generation, employ multiple metrics. We replicated the LongSumm evaluation using multiple test set samples (vs. the single test set of the official shared task) and investigated how different metrics might complement each other in this evaluation framework. We show that under this more rigorous evaluation, (1) some of the key learnings from Longsumm 2020 and 2021 still hold, but the relative ranking of systems changes, and (2) the use of additional metrics reveals additional high-quality summaries missed by ROUGE, and (3) we show that SPICE is a candidate metric for summarization evaluation for LongSumm.

Investigating Metric Diversity for Evaluating Long Document Summarisation

For many NLP applications of online reviews, comparing two opinion-bearing sentences is the key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts. We address this gap by studying: (1) how humans
judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether
or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of “opinion similarity” but not “opinion difference". Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.

Measuring Similarity of Opinion-bearing Sentences

This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the
surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying all constraints. However, they are computationally expensive and can lower the generated text quality. In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. The MF models can be trained to generate tokens in a hypothesis until all constraints are satisfied, guaranteeing high constraint satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Restaurant Dialog task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) show that the MF models maintain higher constraint satisfaction and text quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art performance on all three tasks. These results are achieved with a much lower run-time than constrained decoding algorithms. We also show that the MF models work well in the low-resource setting.

Mention Flags (MF): Constraining Transformer-based Text Generators

Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task. A system that can perform this task has the ability to extend an arbitrary set of relational database tables with information extracted from a document corpus. OpenKi[1] addresses this task through extraction of named entities and predicates via OpenIE tools then learning relation embeddings from the resulting entity-relation graph for relation prediction, outperforming previous approaches. We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations. We demonstrate that this results in a substantial performance increase over a system without this information.


Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction

Click on the Live Session Recording to view the session

**Workshop overview:** As the body of scholarly literature grows, automated methods in NLP, text mining, information retrieval, document understanding etc. are needed to address issues of information overload, disinformation, reproducibility, and more. Though progress has been made, there are significant unique challenges to processing scholarly text that require dedicated attention. The goal of this workshop is to provide a venue for addressing these challenges, as well as a platform for tasks and resources supporting the processing of scientific documents. Our long-term objective is to establish scholarly and scientific texts as an essential domain for NLP research, to supplement current efforts on web text and news articles.

This workshop builds on the success of prior workshops: the SDP workshop held at NAACL in 2021 and EMNLP in 2020, and the SciNLP workshop held at AKBC 2020 and AKBC 2021. Both workshops received numerous submissions and were well attended, demonstrating that interest in this area seems to be growing rapidly. In addition to having broad appeal within the NLP community, we expect that the SDP workshop will attract researchers from other relevant fields including meta-science, scientometrics, data mining, information retrieval, and digital libraries, bringing together these disparate communities within the ACL. [**Read more.**](https://sdproc.org/2022/) Our detailed schedule can be viewed [**here.**](https://sdproc.org/2022/program.html)

W8 Third Workshop on Scholarly Document Processing (SDP 2022)

workshop paper

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.


[![](https://s3.amazonaws.com/pf-upload-01/u-59356/0/2021-09-18/me23j72/button_workshop-website.png)](https://summarization2021.github.io/) 

W21: Third Workshop on New Frontiers in Summarization (NewSum)

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

**Session Chair: ** Nanyun (Violet) Peng 
**Session Volunteer: ** Tiezheng Yu

1B: Language Generation #1

technical paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Alan Ritter

10B-Oral: Information Extraction

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


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Stephen .

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Presentations

Investigating Metric Diversity for Evaluating Long Document Summarisation

Measuring Similarity of Opinion-bearing Sentences

Mention Flags (MF): Constraining Transformer-based Text Generators

Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction

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