Speech vs. Transcript: Does It Matter for Human Annotators in Speech Summarization? (2024)

Abstract: Reference summaries for abstractive speech summarization require human annotation, which can be performed by listening to an audio recording or by reading textual transcripts of the recording. In this paper, we examine whether summaries based on annotators listening to the recordings differ from those based on annotators reading transcripts. Using existing intrinsic evaluation based on human evaluation, automatic metrics, LLM-based evaluation, and a retrieval-based reference-free method. We find that summaries are indeed different based on the source modality, and that speechbased summaries are more factually consistent and information-selective than transcript-based summaries. Meanwhile, transcript-based summaries are impacted by recognition errors in the source, and expert-written summaries are more informative and reliable. We make all the collected data and analysis code public1 to facilitate the reproduction of our work and advance research in this area.

text: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14779–14797 August 11-16, 2024 ©2024 Association for Computational Linguistics
to facilitate the reproduction of our work and advance research in this area.

heading: 1 Introduction
text: Speech summarization (Hori et al., 2002; Rezazadegan et al., 2020) is the task of analyzing long speech recordings and generating a short textual summary that concisely conveys this information. Abstractive speech summarization (Sharma et al., 2022; Palaskar et al., 2021) aims to produce summaries that a human would write (Mani, 1999). Prior work in abstractive speech summarization (Palaskar et al., 2019; Kano et al., 2023; Sharma and Raj, 2022; Sharma et al., 2023b) has focused on developing automatic methods.
However, to the best of our knowledge, there has been little work examining how humans perform speech summarization. Such research is particularly important because the goal of abstractive summarization is to produce human-like summaries,
*Author is now at Google 1https://github.com/cmu-mlsp/interview_humanssum
and having high-quality human summaries is crucial for learning how to automatically summarize, i.e., for model training and evaluation. Also, information about how humans summarize may facilitate improved automatic approaches, and hence it is important to explore the annotation process. In this paper, we delve deeper into the process of human annotation for abstractive speech summarization.
Fig. 1 presents an overview of our paper. Humans can summarize speech when it is presented by (a) listening to the speech directly and then constructing the summary based on this, or (b) reading a textual transcript of the content spoken in the recording, and using that to construct the summary. It is unclear whether summaries based on listening to audio differ from summaries based on reading transcripts. For transcript-based summarization, the method used to obtain the transcription, either Automatic Speech Recognition (ASR) or manual transcription, may also have a significant effect. Additionally, the quality of a summary, regardless of the source modality, is likely to be determined by the level of expertise of the summarizer. To assess the impact of source modality, transcription, and expertise, we formulate the following research questions:
(RQ1) Are summaries based on annotators listening to speech recordings different from summaries based on annotators reading textual transcripts of speech? If so, how?
(RQ2) If ASR transcripts are used instead of manual transcripts, how do recognition errors impact human summaries?
(RQ3) Do summaries written by non-experts summarizers differ from those written by experts, and if so, do the differences vary based on the source modality?
Past work in automatic speech summarization suggests that there are acoustic characteristics in
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speech beyond the textual context that are useful for speech summarization (Kano et al., 2023). This implies that both transcript and raw speech carry complementary information that is useful for summarization. The fact that end-to-end models (Sharma et al., 2022; Kano et al., 2023; Matsuura et al., 2023b,a; Sharma et al., 2023b) that use speech features as input perform differently from cascade models (Kano et al., 2021; Palaskar et al., 2021; Yu et al., 2021; Palaskar et al., 2019) that use speech transcripts (Sharma et al., 2023a) suggests that there are differences in automatic summaries resulting from textual or speech input. In this paper, we study whether the same holds for human annotators.
However, any observations on the impact of reading versus listening for annotation are difficult to make from existing corpora like How2 (Sanabria et al., 2018), SLUE-TED (Shon et al., 2023), Interview (Sharma et al., 2023a), and AMI (McCowan et al., 2005), since it is unclear how human annotations were obtained from speech. Therefore, we select 1002 recordings from the Interview corpus test set and create a new evaluation set. We collect two expert human annotations for transcript-based summarization and two for speech-based summarization per recording and assess how the resulting summaries differ.
Comparing two given summaries is challenging. Broadly, we conduct three types of evaluation: (a) comparing the summary to its source transcript (source-based evaluation) to establish how extractive and factual the summary is, (b) evaluating the summary independently for its structure and grammar (structure evaluation), and (c) by comparing different summaries (summary comparisons) to as-
sess relevance and which represents the input better using semantic similarity, LLM, and human evaluation. Experiments show that speech-based summaries are more information-selective and factually consistent while transcript-based summaries are preferred by human and LLM evaluators.
In real-world settings, human-annotated manual transcripts are not available, and, therefore, Automatic Speech Recognition (ASR) models may be used to generate such transcriptions. ASR transcripts may contain errors, and we investigate the impact of these errors on transcript-based summarization. To do this, we consider 100 recordings from the Interview test set, obtain speech transcriptions using Whisper (Radford et al., 2022), and perform human summarization using 2 expert annotators for each modality. Experiments show that ASR errors in the transcript degrade the quality of human summaries, resulting in lower coherence, fluency, and factual consistency with the source.
Prior work in human evaluation of abstractive text summarization highlights the importance of expertise for summary evaluations (Fabbri et al., 2021b; Gillick and Liu, 2010). However, to the best of our knowledge, there is no prior work that examines the need for expertise in human speech summarization. Therefore, in this work, we examine whether crowd-sourced non-expert summaries are different from expert summaries. To address this question, we obtain 2 non-expert annotations using Amazon Mechanical Turk (AMT) for each of the 1002 recordings from the Interview test set. From these summaries, we analyze (a) differences between expert and non-expert annotations, and (b) whether any differences can be attributed to the approach taken for summary annotation. Analysis
shows that non-expert annotations are less fluent and coherent compared to expert summaries but as factually consistent as expert summaries.
In summary, through this work, we find that transcript-based annotation is valuable if errors are minimal and longer, more informative summaries are desired, while speech-based annotation is desired for higher information selectivity, factual consistency, and resilience to transcription errors.

heading: 2 Related Work
text: For this study, we describe related work in three areas: (a) metrics for comparing summaries, (b) other work investigating the impact of source modality on human performance, and (c) investigations of ASR error impact on Spoken Language Understanding. Comparing Summaries - Metrics: Numerous metrics have been proposed to evaluate the quality of summarization (Fabbri et al., 2021a). Earlier works use metrics like ROUGE-H, ROUGE-L (Zhou et al., 2006), ROUGE-WE (Ng and Abrecht, 2015), BLEU (Papineni et al., 2002), sentence recall/precision is commonly used for simple sentence based extraction summaries (Hirohata et al., 2005). Some studies have found that ROUGE and BLEU-based metrics correlate well with human judgments on summarization. However such scores are not always the best metric for summarization, especially when the input is longer like in meeting summarization and summarization of scientific articles. There have been scores of other metrics which are either variants or combinations of the earlier mentioned metrics like BertScore (Zhang et al., 2019), Sentence Mover’s Similarity (SMS) (Clark et al., 2019), SummaQA (Scialom et al., 2019), SUPERT (Gao et al., 2020), CHRF (Popović, 2015), METEOR (Banerjee and Lavie), CIDEr (Vedantam et al., 2015), or model generated ones like s3 (Peyrard et al., 2017), BLANC (Vasilyev et al., 2020), summACCY (Hori et al., 2003), which are trained to predict the quality of an input summary.
LLMs have been used as an alternative method for summary evaluation. While some work expresses doubt that LLMs have reached a human level of summary evaluation capabilities (Shen et al., 2023), other methods have yielded more optimistic results (Wu et al., 2023). LLM evaluation methods usually take the form of automatic replacements for human evaluations such as Likert scale scoring, pairwise comparison, and binary factuality comparison (Gao et al., 2023). LLMs can also
be used to answer qualitative questions about the factual consistency of a summary and explain the reasoning behind the provided answers (Luo et al., 2023). Source Modality - Transcript vs. Speech: We are also interested in studying whether input modalities have an impact on task performance. Cognitive psychology literature has studied this question under various settings. (Khan et al., 2022) studies cognitive performance and information retention, when note-taking is performed using either voice or text. The paper finds that when the notes are taken using voice versus when they are written down by hand, voice leads to a higher conceptual understanding of the topic. (Stollnberger et al., 2013) studies how input modality effects learning of the English language, specifically whether learning from lecture audio vs reading lecture notes impacts retention. Other papers examine the interaction between input modality (text vs speech) and what users prefer when performing a task. Studies have reached various conclusions regarding the most useful/important modality based on the task at hand, what we do find is that the difference exists and is significant. Impact of ASR on Downstream Tasks: ASR errors are known to have an impact on downstream task performance, like emotion recognition (Schuller et al., 2009; Feng et al., 2020), spoken language understanding (Shon et al., 2023), and Natural language understanding and dialog management (Serdyuk et al., 2018; Shivakumar et al., 2019). Therefore, it is important to assess their impact on human summarization.

heading: 3 Data Collection
text: Datasets that are used for speech summarization today do not have information on which of the two approaches, i.e., reading a textual transcript of the recording or listening to the recording to summarize was employed to obtain annotation. Further, they only have one annotation per recording, which may inhibit analysis due to annotator-specific biases or tendencies. Therefore, to conduct our experiments, we curate a multi-reference dataset from an existing dataset.
We considered a subset from the test partition of the Interview (Sharma et al., 2023a; Majumder et al., 2020; Zhu et al., 2021) corpus for speech summarization. Interview is the largest opendomain corpus with around 5-minute-long speech
recordings containing spontaneous speech along with real audio noises representing music, and events inside and outside the studio corresponding to discussion topics2. Speech recordings contain spontaneous speech along with real audio noises that represent music, and events inside and outside the studio corresponding to discussion topics. The dataset has 49.4k recordings that span multiple topics and domains, with an average recording length of 5.28 minutes. The transcription on average contains 886.4 words and the summary contains 40.1 words on average, representing a compression ratio of 22.09 between the transcript and summary. Around 48% of words in the summary are not present in the transcription, making this a relatively challenging task. On average, each recording has 24 turns and 4 speakers.
Ensuring high-quality summary annotations is crucial to making reliable observations. Therefore, we took the necessary steps to ensure a fair and reliable data collection pipeline. Initially, we consulted with an expert from the in-house data collection team and conducted small pilot studies to make design decisions. Audio Length: We conducted in-house pilot studies comprising six questions by trying to summarize audio recordings that were 2 minutes and 5 minutes long. As expected, summarizing 5-minute recordings consumed more time and effort, and was harder due to the high compression ratio. We concluded that limiting the audio length to 2 minutes would provide sufficient content to summarize without increasing the difficulty level of summarization significantly. Using forced alignment, we obtained audio reference transcripts up to the first 2 minutes. Summary Length: A hard limit was not set on the length of the summary, but it was suggested that summaries should contain at least 2 sentences to convey the essence of the conversation. It was recommended that summaries contain between 50 to 80 words for the 2-minute long recordings. Data Selection: To obtain reasonable summaries, it is important that audio recordings and textual transcripts are understandable, and have a sufficient amount of information to summarize. In cases where background noise or music is present, or when a large portion of the recording does not contain speech, these criteria are violated, lead-
2The authors acquired permission from NPR to use NPR data for this research
ing to sub-optimal recordings for summarization. Therefore, audio recordings were transcribed using Whisper-medium (Radford et al., 2022), and the resulting Word Error Rate (WER) was used to remove unsuitable audio recordings from consideration. Based on manual inspection, a threshold of 25% WER was set, which eliminated recordings with only music or large amounts of noise.
Expert and Non-expert: Our data comprises 1,002 recordings in total, with each recording having a speech recording and its corresponding reference transcript. For each recording, we collected two expert summaries and two non-expert summaries by reading the text or listening to the audio, which gave us a total of 4 summaries based on the text and 4 summaries based on the audio on the same recording. Additionally, we solicited summary annotations from speech recognition transcripts for 100 of these 1002 recordings, sampled at random so the Word Error Rate with Whisper-medium is non-zero, i.e., the transcript has ASR errors.
Annotator Overlap: It is worth noting that each annotator was assigned to work on either the transcript or audio recording for a particular recording, but not both. This approach ensured that no annotator saw audio or transcription for a recording they had already annotated.
Expert Summary Annotation: Expert summary annotations were obtained through a third-party vendor that passed in-house qualitative assessments of summary outputs from a pilot annotation. The third-party vendor had proven their quality of annotation by consistently delivering similar summary annotations for other projects of the team. Detailed annotation guidelines are in Appendix A.
Non-Expert Summary Annotation Non-expert summary annotations were obtained through the Amazon Mechanical Turk (MTurk) platform. Each HIT required the annotator to accept the terms of a consent form that explained the task and requested consent to share responses for reproducible research. Each HIT included four questions, where annotators were asked to write 2 summaries by listening to audio recordings, and two summaries by reading transcript passages. More details are provided in Appendix B.

heading: 4 Evaluating Differences across Summaries
text: Summary evaluation is an inherently challenging problem for general settings since there is often no clear definition of what constitutes a good summary. Within this paper, we deem a summary good if it is factually faithful to and representative of the input source while being abstractive, coherent, and fluent. Therefore, in this section, we outline three types of evaluations.

heading: 4.1 Source-based Evaluation
text: Summary Length and Compression Ratio: Summary length is measured in words and the compression ratio (Mani, 1999) is defined as the number of words in the reference transcript divided by the number of words in the summary. The greater the summary length, typically, the higher the amount of information contained in the summary, and the lower the compression ratio. Novel Words (%): The percentage of words in the summary that are not present within the source transcript, which could be considered to measure the extent of paraphrasing within the summary. Extractiveness: It is a measure of how extractive the summary is and can be approximated by computing lexical overlap using ROUGE-L (Lin, 2004) between the source transcript and the summary. Informativeness-Entities: We propose to compare named entities predicted from the source text and summaries as a proxy for the amount of information contained within the summary. Named entities are extracted using the Entity Recognizer3 from Spacy (Honnibal and Montani, 2017). Informativeness-Semantic Similarity: Also, we compare semantic similarity between the source and summaries using metrics like BERTScore (Zhang et al., 2019) and BARTScore (Yuan et al., 2021). Retrieval-based informativeness: A summary can be considered representative of or informative about the source when it can discriminate the correct source from a selection of all sources. We describe a retrieval-based measure where we compute the semantic embedding similarity (using BERTScore) between the hypothesis summary and all possible source texts. A discriminative summary should produce the highest similarity between the summary and the correct source text. This is measured using retrieval accuracy (RAcc). We also
3https://spacy.io/api/entityrecognizer
report the Mean Reciprocal Rank (MRR) as an indicator of retrieval performance. Factual Consistency: We use UniEval to estimate the factual consistency of summaries with respect to the source transcript.

heading: 4.2 Structure Evaluation
text: To evaluate coherence and fluency automatically, we leverage UniEval (Zhong et al., 2022).

heading: 4.3 Summary Comparisons
text: Pairwise Similarity: We compute relevance and consistency using UniEval (Zhong et al., 2022), and use BARTScore as a measure of semantic similarity. Pairwise scoring is illustrated in Fig. 2, where si is a speech-based summary, tj is a transcriptbased summary, and S(a, b) calculates the score between reference a and hypothesis b (e.g., BART score). The idea behind pairwise scoring is to compare two types of summaries, where one type is a reference and the other a hypothesis. This process results in two scores, a speech reference score, and a transcript reference score. By comparing these two scores, we can identify cases when the information in one type of summary is present within the other type of summary. Pairwise Inter Annotator Agreement (IAA): For text generation tasks, the inter-annotator agreement is challenging to quantify. We use a combination of lexical and semantic similarity metrics like ROUGE-L (Lin, 2004), and BARTScore (Yuan et al., 2021) to automatically represent interannotator agreement. Factual Consistency and Source Representation: We employ LLM-based evaluation and human evaluation to estimate the factual consistency of each summary, and how well each summary represents the source. These evaluation methods are described in detail below.

heading: 4.3.1 LLM-based Evaluation
text: Following the LLM summary evaluation methods listed in Section 2, we use ChatGPT (gpt-3.5-turbo-0125) as a qualitative evaluation method for the summaries collected for this work. Specifically, we designed a multiple choice-style AB test to compare summaries in terms of either factual consistency or accurate representation of the source transcript. The two questions posed to the LLM are displayed in Fig. 3, where the text in the brackets represents the summaries being evaluated and the associated source text. Each question was presented in a separate session to avoid bias from previous questions.
Each question is asked twice for each AB pairing, but with the order in which the summaries are presented reversed to avoid biasing the results. For example, if we were comparing a transcriptbased summary and a speech-based summary, the question would be asked one time with the transcript summary as Summary 1, and then another time with the speech summary as Summary 1. Additionally, since all summaries are collected in pairs, we compare each summary to both summaries from the other group, exhausting all possible combinations. For example, if we were comparing a pair of transcript-based summaries to a pair of speech-based summaries, the first transcript summary would be compared to both speech summaries, and then the second would be compared to both speech summaries, resulting in four total comparisons. Considering both the order of presentation and the comparison combinations, eight questions are posed to the LLM per source sample.

heading: 4.3.2 Human Evaluation
text: To validate the expert summary comparison, we also conducted an AB test for 30 recordings with 15 annotators. We present 2 summaries based on different modalities and their source transcript to a user. We designed 2 multiple-choice questions that ask human annotators to compare summaries based on their factual consistency and how accurately they represent the source transcript. The setup of questions is similar to that in LLM evaluation.

heading: 5 Research Methodology

heading: 5.1 RQ1: Speech versus Text Inputs
text: We apply the methods and metrics described in Section 4 to explore differences between summaries derived from speech recordings and summaries extracted from text transcripts. We use the expert annotations obtained for 1002 recordings for this analysis since expert summaries are more reliable.

heading: 5.2 RQ2: Impact of Transcript Errors on Human Summarization
text: We measure the impact of ASR error propagation on the output summary by comparing a subset of 96 expert summaries given ASR-generated transcripts and ground-truth transcripts. The summaries resulting from the Whisper-generated transcripts are compared to the summaries based on ground-truth transcripts using the methods and metrics outlined in Section 4.
We analyze the effect of ASR errors on the quality of the summaries based on ASR transcripts by plotting informativeness measures like Entity-F1 and WER, and find that as WER increases, EntityF1 decreases.

heading: 5.3 RQ3: Expert versus Non-Expert Annotation
text: We use all methods and metrics from Section 4 to explore the effect of expertise on summary quality. Similar to RQ1, we provide an overall comparison across speech-based summaries as opposed to transcript-based summaries arising from nonexpert annotations and conclude on the reliability of expert and non-expert summaries.

heading: 6 Experimental Results and Discussion

heading: 6.1 RQ1: Speech versus Transcript Inputs
text: Evaluation across the expert summaries, including a comparison between transcript-based and speechbased summaries, is shown in the first two columns of Table 1.
It is immediately apparent that speech-based summaries are significantly shorter and more compressed than transcript-based summaries, indicating that speech-based summaries are highly selective. This is likely because the summarizers extract only the main points when listening to a recording, while a transcript serves as an easier reference for smaller details. This claim is supported by the low level of extractiveness in the speech-based summaries, indicated by a higher percentage of novel words, lower ROUGE-L scores, and a lower percentage of retained named entities. Additionally, coherence for speech-based summaries is relatively low, likely because they are not long or detailed enough to form a very structured summary.
These results also indicate that speech-based summaries are less informative than transcriptbased summaries. Again, this is likely because summarizers can extract details by referencing transcripts much more easily than by searching through the recording. Results that support this claim are high BERT- and BARTScores, high retrieval accuracy, factual consistency, and human and LLM ratings. One interesting observation from the results is that the source retrieval task based on semantic similarity has higher standard deviation scores compared to those observed for other metrics. We note that the proposed retrieval task is challenging compared to simply obtaining similarity scores. Of the source texts in the dataset, some of them are more closely related than others. Hence some retrieval problems are much harder than those that involve retrieval among dissimilar or less similar sources. This phenomenon leads to a higher standard deviation for our retrieval metrics.
The higher pairwise BARTScore for speech references indicates that more of the information within the speech summary is likely present in the transcript-based summary, making speech-based summaries more selective in relevant information. IAA scores computed based on ROUGE-L and BARTScore indicate that speech-based summaries promote higher consensus between raters.
The only result that did not exhibit statistically significant influence from the source modality was fluency. This is reasonable, as the fluency of the model is determined more by the language proficiency of the individual writing the summary than the content.
For RQ1, we find that speech-based summaries seem to be more selective, factually consistent, and perhaps more abstractive than transcript-based summaries, while transcript-based summaries contain more information extracted directly from the source than their speech-based counterparts.

heading: 6.2 RQ2: Impact of Transcript Errors on Human Summarization
text: Table 2 compares the summaries based on the subset of 100 recordings. It is interesting to compare the summaries based on ASR transcriptions to the summaries based on manual transcriptions. Nearly all indicators of informativeness are significantly higher for the manual transcriptions than the ASR transcriptions. This is to be expected, as propagated recognition errors can directly affect measures like Entity F1, factual consistency, and semantic similarity. Coherence is similar for the two types of transcripts, which is consistent with the idea that the ability to reference text allows the
summarizer to capture the details and structure of the source.
Comparing ASR-transcript-based summaries to speech-based summaries, we see similar trends to those in RQ1. Speech-based summaries are still shorter, more selective, less informative, more factually consistent, and likely more abstractive than ASR-transcript-based summaries. In general, LLM evaluation indicates that ChatGPT prefers transcript-based summaries over speech-based summaries, even if the source transcripts contain errors.
Figure 4 contains a scatterplot between the Word Error Rate (WER) of the automatic source transcript and average entity F1 of the humanannotated summary compared to this automatic source transcript. A weak negative correlation is observed between WER of the source transcript and Entity F1, showing that as WER increases, Entity F1 scores tend to decrease on average.
For RQ2, we find that transcription errors decrease the informativeness and coherence of transcript-based summaries, but do not significantly impact fluency.

heading: 6.3 RQ3: Expert Annotation versus Non-Expert Annotation
text: From Table 3, we observe that the speech-based summary length from expert annotation is significantly lower than its non-expert counterpart, while
the expert speech-based and transcript-based summary lengths are very different. The word vocabulary size and the percentage of novel words are lower in the expert summaries (both speech and transcript source) compared to the non-expert summaries.
Extractiveness is lower for non-expert speechbased and transcript-based summaries compared to the expert ones, referring to the fact that the non-experts do not tend to use phrases directly from the source whereas the experts do. Entity F1 score is higher for non-expert speech-based and transcript-based summaries, which is because nonexpert summaries are on average longer than the expert ones and capture more of the entities in the source. BERTscore, BARTscore, retrieval accuracy, and MRR are higher for the expert summaries than the non-expert summaries (for both speech and transcript sources) referring to the fact that non-expert summaries are less informative than the expert summaries. Factual consistency is lower for non-expert summaries pointing to the fact that expert summaries are more factually consistent. Fluency, coherence, and pairwise BARTscores are lower for non-expert summaries demonstrating the reliability of expert summaries.
We ran t-tests to conclude whether the nonexpert speech-based and transcript-based summaries belong to the same distribution and ob-
served based on BARTScore and other metrics that the p-value was high (see Table 4 in Appendix C.2). This potentially indicates that non-expert annotations based on speech and transcripts may be very similar and that some annotators may have used similar methods or tools for the two types of annotations. Based on a human examination, it appears that some annotators did use generative AI to respond to annotation requests through Amazon Mechanical Turk, however, such conclusions may not be definitive since such models can generate human-like speech. This makes non-expert summaries less reliable for analysis of speech-based versus transcript-based summaries, and hence, we decided against running the LLM and human evaluation for these non-expert summaries.
For RQ3, we find that expert annotations are more informative, coherent, fluent, and less information variant across speech-based and transcriptbased summaries.

heading: 7 Conclusion
text: In this paper, we take the first steps towards understanding human annotation for speech summarization by proposing and assessing different approaches. Annotators can either read a transcript of spoken content or listen to the audio recording to produce an abstractive speech summary. We formulate three research questions to assess differences in summaries resulting from variations in source modality (listening to audio versus reading a transcript of spoken content), the nature of transcription (manual versus ASR-based), and the expertise of annotators. Extensive analysis using automatic and human evaluation reveals that speech-based sum-
maries are more information-selective, factually consistent, and abstractive compared to transcriptbased summaries. Analysis of the impact of ASR on transcript-based summarization shows that errors in transcript generation decrease the informativeness and coherence of transcript-based summaries. Finally, analysis of expert and non-expert annotations demonstrates higher informativeness, coherence, and fluency across expert annotations.
Our experiments with non-expert human annotation of transcripts showed that using platforms like Amazon Mechanical Turk may result in less reliable human annotations owing to annotators’ tendency to use generative models like ChatGPT for annotations. Further, our work reaffirms that errors in speech transcription not only impact automatic approaches for summarization but also human annotation. This demonstrates the need for reliable transcripts for speech summarization annotation.
Through this paper, we also develop and release a new human-annotated dataset to enable reproduction and encourage further research in the area. We hope that this study galvanizes further research into human summarization, and powers informed model design for automatic speech summarization.

heading: Ethics Statement
text: We have made a concerted effort to make a positive ethical impact during this work. In data collection, we received permission through the proper channels (IRB) and treated all participants as fairly and respectfully as we could. Through the annotation process, we have valued diversity of opinion and annotator backgrounds and compensated any workers at wages beyond the minimum wage in the US. We have also tried to remove elements of bias by using automatic and manual verifications as appropriate. In the technical work, we have been honest and transparent. We have also carefully considered the implications and potential applications of our work and findings, and we do not believe they present any significant risks to individuals or society as a whole.

heading: Limitations
text: A challenge in the context of this work is that existing methods are designed to evaluate summaries of written text, and may have a bias against speechbased summaries. While some of the methods presented here may begin to address this bias (e.g., retrieval accuracy and IAA metrics), the problem persists. Text-based measures effectively evaluate lexical content, but do not measure properties like emotion, prosody and vocal emphasis which may carry information that is useful for speech summarization. Developing a metric that can be used for both speech- and text inputs remains an ongoing research problem with no consensus solution at this time. Therefore, we utilized the available and broadly accepted text-based evaluation metrics in this paper. Despite the potential bias against speech input based summaries, we still find that in certain aspects, i.e., information selectivity, factualness and abstractiveness, speech-based summaries are beneficial over transcript-based summaries.
Another potentially significant limitation of this paper is the results regarding non-expert summaries. As stated previously, it is possible that the participants on Amazon Mechanical Turk used generative AI software to produce the summaries, so the non-expert analysis is probably not representative of real non-expert summaries.
Broader Impact
Our work represents the first of hopefully many forays into understanding human annotation for
speech summarization. The notion of understanding how humans perform the task, and what would enable them to produce the best possible annotations is both important and understudied. We hope that our new dataset and the analysis we have presented in this work serve to ignite discussions around community standards for speech summarization annotation. Further, we hope that the strengths and weaknesses of summaries based on different approaches inform developers on how to design and deploy the next generation of speech summarizers and more generally speech foundation models.

heading: Appendix

heading: A Expert annotation details

heading: A.1 Annotation guidelines
text: Figure 5 shows the detailed guideline we provided for expert annotation to third-party vendors.

heading: A.2 Annotator Compensation and Statistics
text: In total, there are 36 expert annotators. The expert annotators are all American national and native English speakers, proficient in the language, therefore they don’t have any difficulty performing the summarization task. Among the 36, 34 are female, one identifies as non-binary and one prefers not to say. There exists some variation in their age group, which is shown in the figure 6.
The third-party vendor passed in-house qualitative assessments of summary outputs from a pilot annotation and contracted to the company’s primary third-party vendor for summary annotation. The statement of work has agreements between the company and the vendor, and the company paid 9,341.76 US dollars for a total of 4,208 expert summary annotations. This is a competitive compensation for such expertise, and well beyond the minimum wage within the United States of America, where the annotation was carried out. Annotators consented to the process through the work agreement and were aware of the plans to release the annotations to the public.

heading: A.3 Example Source and Associated Summaries

heading: B Non-Expert annotation details
text: B.1 IRB Review and Informed Consent Non-expert annotations were carried out using Amazon Mechanical Turk. The entire plan for this research paper was presented to an Institutional Review Board (IRB) and received assent before starting data collection. A consent form (shown next) was prepared and presented to prospective annotators, and conditional on their acceptance, they were allowed to take on the annotation task.
Carnegie Mellon University
Version 1.2018
Online Consent Form
This task is part of a research study conducted by Bhiksha Raj at Carnegie Mellon University. The task involves typing short summaries of news excerpts. Each summary will be based on either an audio recording of a news broadcast or a text transcript of a news broadcast. Each set of prompts provided to a participant includes two audio recordings and two transcripts, so four summaries will be written for each set. Each summary should take approximately four minutes to complete, so each set should take about 16 minutes.

heading: Purpose
text: The purpose of the research is to understand whether human annotators write different summaries depending on whether they listen to a recording or read the transcription.

heading: Procedures
text: We will ask participants to listen to recordings and read passages, and then write brief textual summaries based on what they heard or read. We will assign two (2) random audio and two (2) passages for every participant to summarize within 60-80 words.

heading: Participant Requirements
text: Participation in this study is limited to individuals ages 18 and older residing in the United States. Participants should be proficient in English reading, writing, and listening. Participants should not have any hearing impairments that significantly inhibit their ability to listen to audio recordings.

heading: Risks
text: The risks and discomfort associated with participation in this study are no greater than those ordinarily encountered in daily life or during other online activities. Participants should be aware of the potential for a security breach at Amazon Mechanical Turk, which may result in unwanted access to personal information.

heading: Benefits
text: Participants will learn more about writing and develop their summarization skills. They will have learnt about topics discussed in radio interviews, and practiced summary writing based on input knowledge.

heading: Compensation & Costs
text: Compensation will be provided via Amazon Mechanical Turk at a rate of $2.67 per question set (approximately $10 an hour). Each question set should take approximately 16 minutes to complete. Future Use of Information
Carnegie Mellon University
Version 1.2018
In the future, once we have removed all identifiable information from your provided information, we may use the data for our future research studies, or we may distribute the data to other researchers for their research studies. We would do this without getting additional informed consent from you (or your legally authorized representative). Sharing of data with other researchers will only be done in such a manner that you will not be identified.

heading: Confidentiality
text: The only identifiable personal information that will be collected for this task will be your Amazon Mechanical Turk information. By participating in this research, you understand and agree that Carnegie Mellon may be required to disclose your consent form, data and other personally identifiable information as required by law, regulation, subpoena or court order. Otherwise, your confidentiality will be maintained in the following manner: Your data and consent form will be kept separate. Your consent form will be stored in a secure location on Carnegie Mellon property and will not be disclosed to third parties. By participating, you understand and agree that the data and information gathered during this study may be used by Carnegie Mellon and published and/or disclosed by Carnegie Mellon to others outside of Carnegie Mellon. However, your name, address, contact information and other direct personal identifiers will not be mentioned in any such publication or dissemination of the research data and/or results by Carnegie Mellon. Note that per regulation all research data must be kept for a minimum of 3 years.

heading: Right to Ask Questions & Contact Information

heading: If you have any questions about this study, you should feel free to ask them by contacting Roshan Sharma, Department of Electrical and Computer Engineering, Carnegie Mellon University. Email: roshansh@andrew.cmu.edu.

heading: If you have questions later, desire additional information, or wish to withdraw your participation please contact the Principal Investigator by mail, phone or e-mail in accordance with the contact information listed above.
text: If you have questions pertaining to your rights as a research participant; or to report concerns to this study, you should contact the Office of Research integrity and Compliance at Carnegie Mellon University. Email: irb-review@andrew.cmu.edu . Phone: 412-268-1901 or 412-268- 5460.

heading: Voluntary Participation
text: Your participation in this research is voluntary. You may discontinue participation at any time during the research activity. You may print a copy of this consent form for your records.

heading: B.2 Compensation
text: Workers were compensated at a rate of $10/hour for their work, which is above the minimum wage in the United States of America, where the workers reside.

heading: B.3 Worker Selection
text: Only the workers who agree to the consent form are allowed to perform the task on AMT. Furthermore, workers who reside in the US, with an AMT HIT approval rate of greater than 98% and total HITS approved greater than 5000 are selected for the task. Figure 8 shows the criterion used.

heading: B.4 HIT Design and Guidelines
text: Figure 9 shows the layout of the AMT design, how the instructions are presented, and how source audio and text are provided. The worker then proceeds to write the summaries in the text box below the source.
As part of the instructions, the use of generative AI was prohibited to answer the questions. Annotators were asked to write summaries based on the provided inputs while ensuring that the response was coherent and grammatical, and written in their own words. All responses are validated manually for conformity to the instructions, and invalid responses are rejected and re-assigned to a new annotator.

heading: B.5 Demographics of the AMT Workers
text: Figure 10 shows the demographics of the AMT workers. The workers are mostly bachelor graduates, from the 25-34 age bracket, more female than other genders. 952 of the workers are native English speakers
and 41 are not. 1000 of the workers are American nationals, whereas 3 are from elsewhere. We do not collect any information from the workers that can uniquely identify them.

heading: B.6 Data Release
text: We plan to release the summaries annotations collected from experts and non-experts for both modalities under the Apache 2.0 license, subject to the terms of the NPR license. Access to the data will be under the usage policy of NPR and the data is meant to be used for research purposes only.

heading: C Results

heading: C.1 Expert Annotations
text: C.2 Non-Expert Annotations

heading: Future Work
text: Future Use of Information
Carnegie Mellon University
Version 1. 2018
In the future, once we have removed all identifiable information from your provided information, we may use the data for our future research studies, or we may distribute the data to other researchers for their research studies. We would do this without getting additional informed consent from you (or your legally authorized representative). Sharing of data with other researchers will only be done in such a manner that you will not be identified